shhh <- suppressPackageStartupMessages # It's a library, so shhh!

shhh(library( mgcv ))
shhh(library(dplyr))
shhh(library(ggplot2))
shhh(library(lme4))
shhh(library(tidymv))
shhh(library(gamlss))
shhh(library(gsubfn))
shhh(library(lmerTest))
shhh(library(tidyverse))
shhh(library(boot))
shhh(library(rsample))
shhh(library(plotrix))
shhh(library(ggrepel))
shhh(library(mgcv))
shhh(library(brms))
shhh(library(bayesplot))
shhh(library(tidyr))
shhh(library(car))
shhh(library(HDInterval))
shhh(library(gridExtra))
shhh(library(posterior))
shhh(library(readxl))
shhh(library(stringr))
shhh(library(loo))
shhh(library(MASS))
shhh(library(hypr))
shhh(library(designr))
shhh(library(afex))

shhh(library(coda))
shhh(library(rstan))
shhh(library(rstantools))

rstan_options(auto_write=TRUE)
options(mc.cores=parallel::detectCores())
rstan_options(auto_write = TRUE)
theme_set(theme_bw())
options(digits=4)
options(scipen=999)
set.seed(444)

Read in ET Data

file_list <- list.files("/Users/cui/Documents/uzh/PhD/Projects/Russian_Agreement/russian_gender/ref/Eyetracking/", pattern = "*.xlsx", full.names = TRUE)
et_raw <- file_list %>%
  lapply(read_excel) %>%
  bind_rows()

clean ET raw data

select_meas <- c("SFD", "total_duration", "gaze_duration", "FPFix", "go_past_time", "FPReg", "RegIn")

et <- et_raw %>%
  dplyr::select(IA_LABEL, item, word.id, list, RECORDING_SESSION_LABEL, SFD, IA_DWELL_TIME, IA_FIRST_RUN_DWELL_TIME, IA_FIRST_FIX_PROGRESSIVE, IA_SELECTIVE_REGRESSION_PATH_DURATION, IA_REGRESSION_OUT, IA_REGRESSION_IN, gender_match, part, target_gender, type, Region, condition, ACCURACY, animacy) %>%
  rename(
    word = IA_LABEL,
    item_id = item,
    word_nr = word.id,
    subj_id = RECORDING_SESSION_LABEL,
    total_duration = IA_DWELL_TIME,
    gaze_duration = IA_FIRST_RUN_DWELL_TIME,
    first_pass_fix = IA_FIRST_FIX_PROGRESSIVE,
    go_past_time = IA_SELECTIVE_REGRESSION_PATH_DURATION,
    FPReg = IA_REGRESSION_OUT,
    RegIn = IA_REGRESSION_IN,
    AOI_id = Region
  ) %>%
  filter(subj_id != "russ34") %>%  # russ34 has acc 0.6 according to the calculation below.
  mutate(
    go_past_time = as.numeric(go_past_time),
    SFD = if_else(first_pass_fix == 1, SFD, 0),
    gaze_duration = if_else(first_pass_fix == 1, gaze_duration, 0),
    go_past_time = if_else(first_pass_fix == 1,  go_past_time, 0),
  ) %>%
  rename(FPFix = first_pass_fix) %>%
  mutate(
         FPReg = ifelse(gaze_duration==0, NA, FPReg),
         FPFix = ifelse(gaze_duration==0, NA, FPFix)) %>%
  gather(measure, value, select_meas) %>%
  mutate(
    value = as.numeric(value),
    tgt_zero = if_else(measure %in% c("SFD", "gaze_duration", "go_past_time", "total_duration") & value == 0, F, T)) %>%
  filter(tgt_zero != F) %>%
  dplyr::select(-tgt_zero, -condition) %>%
  mutate(item_id = as.factor(item_id),
         subj_id = as.factor(subj_id)) %>%
  spread(measure, value) %>%

  # Note: we commented these lines out when running models because we logged the data and used mix effects to account for the variances and noises. If also filter outliers when running models, the results will not change qualitatively, but the estimated CI (or CrI) will be a bit narrower.
  # We filter outliers only for aesthetic reasons in plotting.

  gather(measure, value, c("SFD", "gaze_duration", "go_past_time", "total_duration")) %>%
  mutate(outlier = value > (mean(value, na.rm = TRUE) + 3 * sd(value, na.rm = TRUE))) %>%
  filter(outlier == FALSE) %>%
  dplyr::select(-outlier) %>%
  spread(measure, value) %>%

  gather(measure, value, select_meas) %>%
  mutate(cond = case_when(
    target_gender == "M" & gender_match == "Mis" & type == "stim_adj" ~ "a",
    target_gender == "M" & gender_match == "Mis" & type == "stim_verb" ~ "b",
     target_gender == "M" & gender_match == "Mis" & type == "stim_pred_adj" ~ "c",
    target_gender == "M" & gender_match == "Match" & type == "stim_adj" ~ "d",
    target_gender == "M" & gender_match == "Match" & type == "stim_verb" ~ "e",
    target_gender == "M" & gender_match == "Match" & type == "stim_pred_adj" ~ "f",
    target_gender == "F" & gender_match == "Mis" & type == "stim_adj" ~ "g",
    target_gender == "F" & gender_match == "Mis" & type == "stim_verb" ~ "h",
    target_gender == "F" & gender_match == "Mis" & type == "stim_pred_adj" ~ "i",
    target_gender == "F" & gender_match == "Match" & type == "stim_adj" ~ "j",
    target_gender == "F" & gender_match == "Match" & type == "stim_verb" ~ "k",
    target_gender == "F" & gender_match == "Match" & type == "stim_pred_adj" ~ "l",
    TRUE ~ NA_character_ # This is the default case if none of the above conditions are met
  )) %>% 
  # filter(animacy %in% c("Inanim", "inanim")) %>% 
  dplyr::select(-list, -part, -animacy) 

et 

Read in MoTR Data

# The path to the data
data_path <- "./data/"
data_names <- list.files(data_path)

# Read in the data from each participant and add to the data frame
motr_df <- data.frame()
for(name in data_names){
  subj <- gsub("reader_", "", gsub("_reading_measures.csv", "", name))
  temp_df <- read.csv(paste0(data_path, "/", name)) %>% mutate(subj_id = subj)
  motr_df <- rbind(motr_df, temp_df)
} 

motr_df <- motr_df %>% mutate(word_len = nchar(word),
                              word_length = scale(word_len)[,1]) %>% 
  group_by(subj_id, item_id) %>%
  arrange(subj_id, item_id) %>%
  mutate(word_len_pre1 = lag(word_length, n = 1),
         word_len_pre2 = lag(word_length, n = 2)) %>%
  ungroup()

# Clean the data
motr <- motr_df %>%
  # filter(subj_id != 171) %>%   # acc = 0.8
  filter(! list %in% c(98, 99)) %>% # filter practice and filler items
  mutate(skip = ifelse(total_duration==0, 1, 0),
         FPReg = ifelse(gaze_duration==0, NA, FPReg),
         FPFix = ifelse(gaze_duration==0, NA, FPFix)) %>%
  filter(skip == 0) %>%
  gather(measure, value, 18:26) %>%
  mutate(tgt_zero = if_else(measure %in% c("first_duration", "gaze_duration", "go_past_time", "right_bounded_rt", "total_duration") & value == 0, F, T)) %>%
  filter(tgt_zero != F) %>%
  dplyr::select(-tgt_zero, -cond_id, -skip, -word_len) %>%
  mutate(item_id = as.factor(item_id),
         subj_id = as.factor(subj_id)) %>%
  spread(measure, value) %>%
  
  # Note: we commented these lines out when running models because we logged the data and used mix effects to account for the variances and noises. If also filter outliers when running models, the results will not change qualitatively, but the estimated CI (or CrI) will be a bit narrower.
  # We filter outliers only for aesthetic reasons in plotting.
  
  gather(measure, value, c("first_duration", "gaze_duration", "go_past_time", "right_bounded_rt", "total_duration")) %>%
  mutate(outlier = value > (mean(value, na.rm = TRUE) + 3 * sd(value, na.rm = TRUE))) %>%
  filter(outlier == FALSE) %>%
  dplyr::select(-outlier) %>%
  spread(measure, value) %>%
  
  gather(measure, value, 21:29) %>%
  mutate(cond = case_when(
    target_gender == "M" & gender_match == "Mis" & type == "stim_adj" ~ "a",
    target_gender == "M" & gender_match == "Mis" & type == "stim_verb" ~ "b",
     target_gender == "M" & gender_match == "Mis" & type == "stim_pred_adj" ~ "c",
    target_gender == "M" & gender_match == "Match" & type == "stim_adj" ~ "d",
    target_gender == "M" & gender_match == "Match" & type == "stim_verb" ~ "e",
    target_gender == "M" & gender_match == "Match" & type == "stim_pred_adj" ~ "f",
    target_gender == "F" & gender_match == "Mis" & type == "stim_adj" ~ "g",
    target_gender == "F" & gender_match == "Mis" & type == "stim_verb" ~ "h",
    target_gender == "F" & gender_match == "Mis" & type == "stim_pred_adj" ~ "i",
    target_gender == "F" & gender_match == "Match" & type == "stim_adj" ~ "j",
    target_gender == "F" & gender_match == "Match" & type == "stim_verb" ~ "k",
    target_gender == "F" & gender_match == "Match" & type == "stim_pred_adj" ~ "l",
    TRUE ~ NA_character_ # This is the default case if none of the above conditions are met
  )) %>%
  dplyr::select(-list, -part, -type_id, -orig_item_number, -case, -animacy, -response_true, -response_chosen) %>%
  mutate(word = str_replace_all(word, "\\.", "")) %>%
  rowwise() %>%
  mutate(log_freq = ifelse(word %in% et_raw$IA_LABEL, 
                           et_raw$lg_frequency[match(word, et_raw$IA_LABEL)], 
                           NA_real_)) %>%
  ungroup()

# View(motr)

ACC by participant

motr_acc <- motr %>% dplyr::select(item_id, cond, subj_id, correctness) %>%
  filter(correctness != 99) %>%
  distinct()

motr_acc_summary <- motr_acc %>%
  group_by(subj_id) %>%
  summarise(mean_correctness = mean(correctness),
            sd_correctness = sd(correctness),
            count = n())

# only subj_id 171 get acc = 0.8; others all > 0.88 (incl. fillers)

# write.csv(motr_acc_summary, "./stats/correctness_summary.csv", row.names = FALSE)

et_acc <- et %>% dplyr::select(all_of(c("item_id", "subj_id", "cond", "ACCURACY"))) %>%
  # filter(ACCURACY != -1) %>%
  distinct() %>%
  mutate(correctness = as.numeric(unlist(ACCURACY)))

et_acc_summary <- et_acc %>%
  group_by(subj_id) %>%
  summarise(mean_correctness = mean(correctness),
            sd_correctness = sd(correctness),
            count = n())

# only subj_id russ34 get acc = 0.6; others all > 0.8 (excl. fillers)

ACC by item

motr_acc_cond <- motr_acc %>%
  group_by(cond) %>%
  summarise(
    mean_correctness = round(mean(correctness), 2),
    sd_correctness = round(sd(correctness), 2),
    count = n()
  )
motr_acc_cond

et_acc_cond <- et_acc %>%
  group_by(cond) %>%
  summarise(
    mean_correctness = round(mean(correctness), 2),
    sd_correctness = round(sd(correctness), 2),
    count = n()
  )

et_acc_cond

RESEARCH QUESTIONS:

  1. Are RTs significantly different between gender-match and gender-mismatch conditions? ==> main effect of grammaticality (gender match or not)

  2. Are RTs different in Masculine versus Feminine sentence conditions? ==> main effect of gender of target word

  3. Are RTs affected by lexical category (whether different lexical categories of the agreeing element will make the processing more difficult or not)? –> ADJ(adj & pre_adj) v.s. VERB ==> main effect of lexical type of sentences.

  4. Are RTs affected by feature matching mechanism(whether agreeing element instantiates internal v.s. external agreement will make a difference in processing difficulty)? –> External (verb & predicative adjective) v.s. Internal (modifying adjective) ==> main effect of syntax type of sentences.

  5. Whether the effect of grammaticality is modulated by lexical category? –> Is the RT difference caused by grammaticality effect affected by the target word being Adj or Verb? ==> interaction between grammaticality and lexical type

  6. Whether the effect of grammaticality depends on the feature matching mechanism of the sentence? –> Is the RT difference caused by grammaticality effect affected by the mechanism being external or internal? ==> interaction between grammaticality and feature matching mechanism

  7. Does the (possible) difference in the sensitivity to the grammaticality manipulation of Masculine versus Feminine conditions differ between lexical category (Adj v.s. Verb)? ==> 3-way interaction between grammaticality, gender and lexical category

  8. Does the (possible) difference in the sensitivity to the grammaticality manipulation of Masculine versus Feminine conditions differ between feature matching mechanism (External v.s. Internal)? ==> 3-way interaction between grammaticality, gender and feature matching mechanism

contrast coding

# check conditions
et$cond <- factor(et$cond)
levels(et$cond)
 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"
motr$cond <- factor(motr$cond)
levels(motr$cond)
 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"

Create hypothesis matrix from RQ

## sol1 
X_H <- matrix(c(1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12, # Intercept
                1/6,1/6,1/6,-1/6,-1/6,-1/6,1/6,1/6,1/6,-1/6,-1/6,-1/6, # Main effect of grammaticality
                1/6,1/6,1/6,1/6,1/6,1/6,-1/6,-1/6,-1/6,-1/6,-1/6,-1/6, # Main effect of gender
                1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8, # Main effect of feature matching
                -1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8, # Main effect of lexical category
                1/6,1/6,1/6,-1/6,-1/6,-1/6,-1/6,-1/6,-1/6,1/6,1/6,1/6, # gram x gen
                1/4,-1/8,-1/8,-1/4,1/8,1/8,1/4,-1/8,-1/8,-1/4,1/8,1/8, # gram x synt
                -1/8,1/4,-1/8,1/8,-1/4,1/8,-1/8,1/4,-1/8,1/8,-1/4,1/8, # gram x lex
                1/4,-1/8,-1/8,1/4,-1/8,-1/8,-1/4,1/8,1/8,-1/4,1/8,1/8,  #gen x synt
                -1/8,1/4,-1/8,-1/8,1/4,-1/8,1/8,-1/4,1/8,1/8,-1/4,1/8, # gen x lex
                1/2,-1/4,-1/4,-1/2,1/4,1/4,-1/2,1/4,1/4,1/2,-1/4,-1/4,  # gram x gen x synt
                -1/4,1/2,-1/4,1/4,-1/2,1/4,1/4,-1/2,1/4,-1/4,1/2,-1/4 # gram x gen x lex
                
), byrow=TRUE, nrow = 12)
# X_H
# rowSums(X_H) # ensure centering

X_C = ginv(X_H)
rownames(X_C) <- c('a','b','c','d','e','f','g','h', 'i', 'j', 'k', 'l')
colnames(X_C) <- c('Int','Gram','Gen','Lex','Synt','Gram_x_Gen','Gram_x_Lex','Gram_x_Synt','Gen_x_Lex','Gen_x_synt','Gram_x_Gen_Lex','Gram_x_Gen_Synt')
X_C_bar <- X_C[,2:ncol(X_C)]
fractions(X_C_bar)
  Gram Gen  Lex  Synt Gram_x_Gen Gram_x_Lex Gram_x_Synt Gen_x_Lex Gen_x_synt Gram_x_Gen_Lex
a  1/2  1/2  2/3    0  1/2        2/3          0         2/3         0        1/3          
b  1/2  1/2    0  2/3  1/2          0        2/3           0       2/3          0          
c  1/2  1/2 -2/3 -2/3  1/2       -2/3       -2/3        -2/3      -2/3       -1/3          
d -1/2  1/2  2/3    0 -1/2       -2/3          0         2/3         0       -1/3          
e -1/2  1/2    0  2/3 -1/2          0       -2/3           0       2/3          0          
f -1/2  1/2 -2/3 -2/3 -1/2        2/3        2/3        -2/3      -2/3        1/3          
g  1/2 -1/2  2/3    0 -1/2        2/3          0        -2/3         0       -1/3          
h  1/2 -1/2    0  2/3 -1/2          0        2/3           0      -2/3          0          
i  1/2 -1/2 -2/3 -2/3 -1/2       -2/3       -2/3         2/3       2/3        1/3          
j -1/2 -1/2  2/3    0  1/2       -2/3          0        -2/3         0        1/3          
k -1/2 -1/2    0  2/3  1/2          0       -2/3           0      -2/3          0          
l -1/2 -1/2 -2/3 -2/3  1/2        2/3        2/3         2/3       2/3       -1/3          
  Gram_x_Gen_Synt
a    0           
b  1/3           
c -1/3           
d    0           
e -1/3           
f  1/3           
g    0           
h -1/3           
i  1/3           
j    0           
k  1/3           
l -1/3           
contr_motr <- motr %>% 
  mutate(
    #--------------------- main effects ---------------------
    Gram = ifelse(cond %in% c('a', 'b', 'c', 'g', 'h', 'i'), 1/2, -1/2), # Main effect grammaticality 
    Gen = ifelse(cond %in% c('a','b','c','d','e', 'f'), 1/2, -1/2), # Main effect gender
    Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of feature matching  (a vs pv)
    Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of lexical category (ap vs v)
    
    #--------------------- 2-way interactions ---------------------
    Gram_x_Gen = ifelse(cond %in% c('a', 'b', 'c', 'j', 'k', 'l'), 1/2, -1/2), # Grammaticality x Gender
    Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'g', 'j'), -2/3, 2/3)), # Gender x Feature matching
    Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'h', 'k'), -2/3, 2/3)), # Gender x Lexical category
    Gram_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'i', 'j'), -2/3, 2/3)), # Grammaticality x Feature matching
    Gram_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'i', 'k'), -2/3, 2/3)), # Grammaticality x Lexical Category
    
    #--------------------- 3 way interection ---------------------
    Gram_x_Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'g', 'l'), -1/3, 1/3)), # gen x synt(ap v) x gram
    Gram_x_Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'h', 'l'), -1/3, 1/3)) # gen x lex(ap v) x gram
  ) %>% spread(measure, value) #%>%
  # # filter(word_nr == 3)
  # filter(AOI_id == "R3")
  
contr_motr

contr_et <- et %>% 
  mutate(
    #--------------------- main effects ---------------------
    Gram = ifelse(cond %in% c('a', 'b', 'c', 'g', 'h', 'i'), 1/2, -1/2), # Main effect grammaticality 
    Gen = ifelse(cond %in% c('a','b','c','d','e', 'f'), 1/2, -1/2), # Main effect gender
    Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of feature matching  (a vs pv)
    Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of lexical category (v vs ap)
    
    #--------------------- 2-way interactions ---------------------
    Gram_x_Gen = ifelse(cond %in% c('a', 'b', 'c', 'j', 'k', 'l'), 1/2, -1/2), # Grammaticality x Gender
    Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'g', 'j'), -2/3, 2/3)), # Gender x Feature matching
    Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'h', 'k'), -2/3, 2/3)), # Gender x Lexical category
    Gram_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'i', 'j'), -2/3, 2/3)), # Grammaticality x Feature matching
    Gram_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'i', 'k'), -2/3, 2/3)), # Grammaticality x Lexical Category
    
    #--------------------- 3 way interection ---------------------
    Gram_x_Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'g', 'l'), -1/3, 1/3)), # gen x synt(ap v) x gram
    Gram_x_Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'h', 'l'), -1/3, 1/3)) # gen x lex(ap v) x gram
  ) %>% spread(measure, value) %>%
  rename(RegIn_incl = RegIn)
# View(contr_et)
write.csv(contr_et, "./stats/et_reading_measures_contrast_coded.csv", row.names = FALSE)
write.csv(contr_motr, "./stats/motr_reading_measures_contrast_coded.csv", row.names = FALSE)
## sol2 --> try hypr package, also for sanity check
hypothesis_matrix <- hypr(
  Gram = (a+b+c+g+h+i)/6 ~ (d+e+f+j+k+l)/6,
  Gen = (a+b+c+d+e+f)/6 ~ (g+h+i+j+k+l)/6,
  Synt = (a+d+g+j)/4 ~ (b+c+e+f+h+i+k+l)/8,
  Lex = (b+e+h+k)/4 ~ (a+c+d+f+g+i+j+l)/8,
  # Gram_x_Gen = ((a+b+c)/3-(d+e+f)/3)/2 ~ ((g+h+i)/3-(j+k+l)/3)/2,
  Gram_x_Synt = ((e+f+k+l)/4-(d+j)/2)/2 ~ ((b+c+h+i)/4-(a+g)/2)/2 ,
  Gram_x_Lex =  ((d+f+j+l)/4-(e+k)/2)/2 ~ ((a+c+g+i)/4-(b+h)/2)/2,
  # Gen_x_Synt = ((h+i+k+l)/4-(g+j)/2)/2 ~ ((b+c+e+f)/4-(a+d)/2)/2,
  # Gen_x_Lex = ((g+i+j+l)/4-(h+k)/2)/2 ~ ((a+c+d+f)/4-(b+e)/2)/2,
  Gram_x_Gen_x_Synt = (((h+i)/2-g)-((k+l)/2-j))/2 ~ (((b+c)/2-a)-((e+f)/2-d))/2,
  Gram_x_Gen_x_Lex = (((g+i)/2-h)-((j+l)/2-k))/2 ~ (((a+c)/2-b)-((d+f)/2-e))/2
)

# Display the matrix
hypothesis_matrix
hypr object containing 8 null hypotheses:
             H0.Gram: 0 = (a + b + c + g + h + i - d - e - f - j - k - l)/6
              H0.Gen: 0 = (a + b + c + d + e + f - g - h - i - j - k - l)/6
             H0.Synt: 0 = (a + d + g + j - 1/2*b - 1/2*c - 1/2*e - 1/2*f - 1/2*h - 1/2*i - 1/2*k - 1/2*l)/4
              H0.Lex: 0 = (b + e + h + k - 1/2*a - 1/2*c - 1/2*d - 1/2*f - 1/2*g - 1/2*i - 1/2*j - 1/2*l)/4
      H0.Gram_x_Synt: 0 = (1/2*e + 1/2*f + 1/2*k + 1/2*l - d - j - 1/2*b - 1/2*c - 1/2*h - 1/2*i + a + g)/4
       H0.Gram_x_Lex: 0 = (1/2*d + 1/2*f + 1/2*j + 1/2*l - e - k - 1/2*a - 1/2*c - 1/2*g - 1/2*i + b + h)/4
H0.Gram_x_Gen_x_Synt: 0 = (1/2*h + 1/2*i - g - 1/2*k - 1/2*l + j - 1/2*b - 1/2*c + a + 1/2*e + 1/2*f - d)/2
 H0.Gram_x_Gen_x_Lex: 0 = (1/2*g + 1/2*i - h - 1/2*j - 1/2*l + k - 1/2*a - 1/2*c + b + 1/2*d + 1/2*f - e)/2

Call:
hypr(Gram = ~1/6 * a + 1/6 * b + 1/6 * c + 1/6 * g + 1/6 * h + 
    1/6 * i - 1/6 * d - 1/6 * e - 1/6 * f - 1/6 * j - 1/6 * k - 
    1/6 * l, Gen = ~1/6 * a + 1/6 * b + 1/6 * c + 1/6 * d + 1/6 * 
    e + 1/6 * f - 1/6 * g - 1/6 * h - 1/6 * i - 1/6 * j - 1/6 * 
    k - 1/6 * l, Synt = ~1/4 * a + 1/4 * d + 1/4 * g + 1/4 * 
    j - 1/8 * b - 1/8 * c - 1/8 * e - 1/8 * f - 1/8 * h - 1/8 * 
    i - 1/8 * k - 1/8 * l, Lex = ~1/4 * b + 1/4 * e + 1/4 * h + 
    1/4 * k - 1/8 * a - 1/8 * c - 1/8 * d - 1/8 * f - 1/8 * g - 
    1/8 * i - 1/8 * j - 1/8 * l, Gram_x_Synt = ~1/8 * e + 1/8 * 
    f + 1/8 * k + 1/8 * l - 1/4 * d - 1/4 * j - 1/8 * b - 1/8 * 
    c - 1/8 * h - 1/8 * i + 1/4 * a + 1/4 * g, Gram_x_Lex = ~1/8 * 
    d + 1/8 * f + 1/8 * j + 1/8 * l - 1/4 * e - 1/4 * k - 1/8 * 
    a - 1/8 * c - 1/8 * g - 1/8 * i + 1/4 * b + 1/4 * h, Gram_x_Gen_x_Synt = ~1/4 * 
    h + 1/4 * i - 1/2 * g - 1/4 * k - 1/4 * l + 1/2 * j - 1/4 * 
    b - 1/4 * c + 1/2 * a + 1/4 * e + 1/4 * f - 1/2 * d, Gram_x_Gen_x_Lex = ~1/4 * 
    g + 1/4 * i - 1/2 * h - 1/4 * j - 1/4 * l + 1/2 * k - 1/4 * 
    a - 1/4 * c + 1/2 * b + 1/4 * d + 1/4 * f - 1/2 * e, levels = c("a", 
"b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"))

Hypothesis matrix (transposed):
  Gram Gen  Synt Lex  Gram_x_Synt Gram_x_Lex Gram_x_Gen_x_Synt Gram_x_Gen_x_Lex
a  1/6  1/6  1/4 -1/8  1/4        -1/8        1/2              -1/4            
b  1/6  1/6 -1/8  1/4 -1/8         1/4       -1/4               1/2            
c  1/6  1/6 -1/8 -1/8 -1/8        -1/8       -1/4              -1/4            
d -1/6  1/6  1/4 -1/8 -1/4         1/8       -1/2               1/4            
e -1/6  1/6 -1/8  1/4  1/8        -1/4        1/4              -1/2            
f -1/6  1/6 -1/8 -1/8  1/8         1/8        1/4               1/4            
g  1/6 -1/6  1/4 -1/8  1/4        -1/8       -1/2               1/4            
h  1/6 -1/6 -1/8  1/4 -1/8         1/4        1/4              -1/2            
i  1/6 -1/6 -1/8 -1/8 -1/8        -1/8        1/4               1/4            
j -1/6 -1/6  1/4 -1/8 -1/4         1/8        1/2              -1/4            
k -1/6 -1/6 -1/8  1/4  1/8        -1/4       -1/4               1/2            
l -1/6 -1/6 -1/8 -1/8  1/8         1/8       -1/4              -1/4            

Contrast matrix:
  Gram Gen  Synt Lex  Gram_x_Synt Gram_x_Lex Gram_x_Gen_x_Synt Gram_x_Gen_x_Lex
a  1/2  1/2  2/3    0  2/3           0        1/3                 0            
b  1/2  1/2    0  2/3    0         2/3          0               1/3            
c  1/2  1/2 -2/3 -2/3 -2/3        -2/3       -1/3              -1/3            
d -1/2  1/2  2/3    0 -2/3           0       -1/3                 0            
e -1/2  1/2    0  2/3    0        -2/3          0              -1/3            
f -1/2  1/2 -2/3 -2/3  2/3         2/3        1/3               1/3            
g  1/2 -1/2  2/3    0  2/3           0       -1/3                 0            
h  1/2 -1/2    0  2/3    0         2/3          0              -1/3            
i  1/2 -1/2 -2/3 -2/3 -2/3        -2/3        1/3               1/3            
j -1/2 -1/2  2/3    0 -2/3           0        1/3                 0            
k -1/2 -1/2    0  2/3    0        -2/3          0               1/3            
l -1/2 -1/2 -2/3 -2/3  2/3         2/3       -1/3              -1/3            
stats_freq = data.frame()
# regions = c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions = c("R3")
measure_types = c("gaze_duration", "go_past_time", "total_duration",
"FPReg", "RegIn_incl"
)

for (meth in methods) {
  for (region in regions) {
    for (meas in measure_types){
      print(paste("Fitting model for:", meas, "in Region:", region))
      if (meas %in% c("first_duration", "gaze_duration", "go_past_time", "total_duration")){
        if (meth == "motr") {
          model <- contr_motr %>% 
            filter(AOI_id == region) %>% 
            filter(!is.na(.data[[meas]]))  %>% 
            lmer(as.formula(paste("log(", meas, ") ~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
                (1 | item_id) + (1 + Gram | subj_id)")),
                data = ., REML = F)
        } else {
          model <- contr_et %>% 
            filter(AOI_id == region) %>% 
            filter(!is.na(.data[[meas]]))  %>% 
            lmer(as.formula(paste("log(", meas, ") ~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
                (1 | item_id) + (1 + Gram | subj_id)")),
                data = ., REML = F)
        }
      coefs <- summary(model)$coefficients
      temp_results <- data.frame(
        method = meth,
        region = region,
        measure = meas,
        beta = c("b_0", "b_Gram", "b_Gen", "b_Lex", "b_Synt", 
                 "b_Gram_x_Lex", "b_Gram_x_Synt", "b_Gram_x_Gen_x_Lex", "b_Gram_x_Gen_x_Synt"),
        bval = coefs[, "Estimate"],
        pval = coefs[, "Pr(>|t|)"]
          )
      }else{
        if (meth == "motr") {
          model <- contr_motr %>% filter(!is.na(.data[[meas]]))  %>% 
            glmer(as.formula(paste(meas, "~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt + 
                (1 | item_id) + (1 | subj_id)")), 
                data = ., family=binomial(link = "logit"))
        } else{
          model <- contr_et %>% filter(!is.na(.data[[meas]]))  %>% 
            glmer(as.formula(paste(meas, "~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt + 
                (1 | item_id) + (1 | subj_id)")), 
                data = ., family=binomial(link = "logit"))
        }
      coefs <- summary(model)$coefficients
      temp_results <- data.frame(
        method = meth,
        region = region,
        measure = meas,
        beta = c("b_0", "b_Gram", "b_Gen", "b_Lex", "b_Synt", 
                 "b_Gram_x_Lex", "b_Gram_x_Synt", "b_Gram_x_Gen_x_Lex", "b_Gram_x_Gen_x_Synt"),
        bval = coefs[, "Estimate"],
        pval = coefs[, "Pr(>|z|)"]
        )
      }
    stats_freq = rbind(stats_freq, temp_results)
  }
}
}
stats_freq <- stats_freq %>%
  mutate(sig = case_when(
    pval < 0.001 ~ "***",
    pval < 0.01  ~ "**",
    pval < 0.05  ~ "*",
    pval < 0.1   ~ ".",
    TRUE         ~ ""
  ))

# View(stats_freq)

# write.csv(stats_freq, "./stats/stats_motr_et_lmer.csv", row.names = FALSE)

Fit Bayesian models

function for creating stan data format

createStanData <-function(d, dv,form){
  
  subj <- as.integer(factor(d$subj_id))
  N_subj <- length(unique(subj))
  item <- as.integer(factor(d$item_id))
  N_items <- length(unique(item))
  X <- unname(model.matrix(form, d))  
  attr(X, which="assign") <- NULL
  
  stanData <- list(N = nrow(X),           
                  P = ncol(X),              
                  n_u = ncol(X),             
                  n_w = 3,            
                  X = X,                     
                  Z_u = X,                 
                  Z_w = X[, 1:3, drop = FALSE],    # only by-item random intercept, Gram, Gen              
                  J = N_subj,                
                  K = N_items,
                  dv = dv,                    
                  subj = subj,
                  item = item)
  stanData
}
# note: this chunk takes time to run ~ 1 hour for one region

# regions <- c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions <- c("R3")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
# measure_types <- c("go_past_time")

for (meth in methods) {
  for (region in regions) {
    for (meas in measure_types) {
      print(paste("Fitting Bayesian model for:", meas, "in Region:", region))
      if (meth == "motr"){
        # Filter data for current region and non-missing measure
        temp <- contr_motr %>%
          filter(AOI_id == region) %>%
          filter(!is.na(.data[[meas]]))
      } else {
        temp <- contr_et %>%
          filter(AOI_id == region) %>%
          filter(!is.na(.data[[meas]]))
      }
      # binary dv
      if (meas %in% c("FPReg", "RegIn_incl")) {
        stan_data <- createStanData(
          d = temp,
          form = as.formula("~1 + Gram + Gen + Synt + Lex + Gram_x_Synt + Gram_x_Lex + Gram_x_Gen_x_Synt + Gram_x_Gen_x_Lex"), 
          dv = temp[[meas]]
        )
       stan_model_file <- "stan/Model_binary.stan"
      } else {
        # For other measures, use the default formulas and models
        stan_data <- createStanData(
          d = temp,
          form = as.formula("~1 + Gram + Gen + Synt + Lex + Gram_x_Synt + Gram_x_Lex + Gram_x_Gen_x_Synt + Gram_x_Gen_x_Lex"), 
          dv = temp[[meas]]
        )
        stan_model_file <- "stan/Model_RT.stan"
      }
      # Fit model 
      stan_model <- stan(
        file = stan_model_file, 
        data = stan_data,
        iter = 4000, 
        chains = 4,
        control = list(adapt_delta = 0.99)
      )
      # Save model output
      model_save_path <- paste0("models/", meth, "_", meas, "_", region, ".rds")
      saveRDS(stan_model, file = model_save_path)
    }
  }
}

Examine fitted stan models

# change xx.rds to other models to check them
region <- "R3"
meas <- "go_past_time"

model_path <- paste0("models/et_", meas, "_", region, ".rds")
m1_gd <- readRDS(model_path)

summary(m1_gd)
$summary
                           mean    se_mean        sd          2.5%            25%             50%
beta[1]               5.7137276 0.00116026   0.04449     5.6255388     5.68434020     5.713520936
beta[2]               0.1278191 0.00028141   0.02518     0.0767242     0.11149445     0.127758066
beta[3]              -0.0110824 0.00022626   0.02377    -0.0582467    -0.02680162    -0.011386977
beta[4]              -0.0512615 0.00064983   0.03962    -0.1301893    -0.07721791    -0.051252348
beta[5]              -0.1106493 0.00063248   0.04011    -0.1865604    -0.13833594    -0.111422194
beta[6]               0.0117341 0.00022192   0.02214    -0.0312682    -0.00309903     0.011362633
beta[7]              -0.0248344 0.00021342   0.02185    -0.0669826    -0.03937759    -0.024796391
beta[8]              -0.0045650 0.00035250   0.03875    -0.0806718    -0.03032326    -0.004716087
beta[9]              -0.0206183 0.00033778   0.03779    -0.0954384    -0.04559835    -0.020265754
sigma_e               0.3921495 0.00007697   0.00720     0.3785931     0.38721123     0.391947750
sigma_u[1]            0.2529664 0.00075530   0.03213     0.1990411     0.23037704     0.249860362
sigma_u[2]            0.1053344 0.00069806   0.03168     0.0397446     0.08587754     0.105457676
sigma_u[3]            0.0210309 0.00019291   0.01639     0.0008581     0.00809264     0.017342948
sigma_u[4]            0.0264723 0.00028287   0.01993     0.0011474     0.01047744     0.022516511
sigma_u[5]            0.0370721 0.00042782   0.02568     0.0017461     0.01621900     0.033231597
sigma_u[6]            0.0402110 0.00044760   0.02594     0.0019864     0.01902661     0.037557373
sigma_u[7]            0.0290649 0.00033137   0.02160     0.0011088     0.01168992     0.025014587
sigma_u[8]            0.0700658 0.00078836   0.04788     0.0032722     0.03151649     0.062843893
sigma_u[9]            0.0504653 0.00051471   0.03727     0.0022300     0.02067267     0.043085240
sigma_w[1]            0.1215831 0.00030786   0.01715     0.0912811     0.10935583     0.120383139
sigma_w[2]            0.0455099 0.00049511   0.02889     0.0019983     0.02207275     0.043196198
sigma_w[3]            0.0944788 0.00079417   0.03456     0.0181357     0.07367294     0.096009703
L_u[1,1]              1.0000000        NaN   0.00000     1.0000000     1.00000000     1.000000000
L_u[1,2]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[1,3]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[1,4]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[1,5]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[1,6]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[1,7]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[1,8]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[1,9]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[2,1]              0.1552419 0.00215168   0.19956    -0.2491587     0.02349424     0.160363298
L_u[2,2]              0.9665586 0.00056708   0.04296     0.8470914     0.95302364     0.983105894
L_u[2,3]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[2,4]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[2,5]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[2,6]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[2,7]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[2,8]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[2,9]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[3,1]             -0.0359758 0.00215963   0.28366    -0.5744168    -0.23447634    -0.039973263
L_u[3,2]              0.0209408 0.00220376   0.28972    -0.5337836    -0.18367019     0.025472455
L_u[3,3]              0.9094959 0.00155353   0.08187     0.6979178     0.87061704     0.932317628
L_u[3,4]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[3,5]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[3,6]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[3,7]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[3,8]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[3,9]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[4,1]             -0.0132876 0.00226170   0.27396    -0.5313123    -0.20401133    -0.015092069
L_u[4,2]              0.0077878 0.00223027   0.28129    -0.5460384    -0.19074487     0.009194853
L_u[4,3]             -0.0089796 0.00237496   0.29254    -0.5614333    -0.22141558    -0.008826501
L_u[4,4]              0.8660699 0.00172079   0.09939     0.6193810     0.81233656     0.888783980
L_u[4,5]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[4,6]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[4,7]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[4,8]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[4,9]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[5,1]             -0.0314604 0.00218045   0.25860    -0.5123615    -0.21454363    -0.034753994
L_u[5,2]             -0.0077931 0.00252056   0.27874    -0.5429627    -0.20973779    -0.007226191
L_u[5,3]             -0.0151788 0.00290837   0.29238    -0.5705059    -0.22303423    -0.019483430
L_u[5,4]             -0.0109113 0.00301111   0.29182    -0.5716054    -0.21879207    -0.011878085
L_u[5,5]              0.8190122 0.00177359   0.11243     0.5472738     0.75386858     0.839161068
L_u[5,6]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[5,7]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[5,8]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[5,9]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[6,1]              0.1699310 0.00241785   0.26811    -0.3915943    -0.00623231     0.191516332
L_u[6,2]             -0.1232831 0.00289328   0.27899    -0.6195553    -0.32823579    -0.136666671
L_u[6,3]             -0.0056129 0.00279400   0.28560    -0.5533982    -0.20598338    -0.006156572
L_u[6,4]              0.0049108 0.00271092   0.28134    -0.5368213    -0.19549189     0.006578870
L_u[6,5]             -0.0144534 0.00279867   0.28122    -0.5379807    -0.21668980    -0.015900686
L_u[6,6]              0.7407234 0.00212625   0.13238     0.4467269     0.65521301     0.755071546
L_u[6,7]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[6,8]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[6,9]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[7,1]              0.0225342 0.00231310   0.27254    -0.5055719    -0.16540892     0.020566509
L_u[7,2]             -0.0173667 0.00249217   0.28622    -0.5641868    -0.22264554    -0.017735141
L_u[7,3]             -0.0056213 0.00253197   0.29154    -0.5626096    -0.21212310    -0.008479741
L_u[7,4]             -0.0194204 0.00249284   0.29218    -0.5691138    -0.22475292    -0.018512991
L_u[7,5]             -0.0115889 0.00259252   0.29089    -0.5593560    -0.22242404    -0.018337900
L_u[7,6]             -0.0505733 0.00261831   0.29493    -0.6122371    -0.26017809    -0.055786325
L_u[7,7]              0.6910385 0.00254748   0.14303     0.3766181     0.59996601     0.706481272
L_u[7,8]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[7,9]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[8,1]             -0.0187178 0.00195627   0.26327    -0.5253724    -0.20407320    -0.021178999
L_u[8,2]              0.1322966 0.00264911   0.27673    -0.4278802    -0.05991167     0.144514676
L_u[8,3]             -0.0101853 0.00282423   0.28970    -0.5544915    -0.21774771    -0.010900006
L_u[8,4]             -0.0136163 0.00278354   0.28947    -0.5625898    -0.22088930    -0.012913273
L_u[8,5]              0.0316614 0.00282775   0.28592    -0.5346124    -0.16834414     0.037165430
L_u[8,6]             -0.0517191 0.00290483   0.28517    -0.5878236    -0.25370805    -0.060174047
L_u[8,7]             -0.0100569 0.00287979   0.29450    -0.5785394    -0.22076021    -0.007100980
L_u[8,8]              0.6259864 0.00233419   0.15127     0.3118438     0.52113413     0.634421109
L_u[8,9]              0.0000000        NaN   0.00000     0.0000000     0.00000000     0.000000000
L_u[9,1]              0.1099632 0.00222724   0.28671    -0.4739303    -0.08856047     0.124010155
L_u[9,2]             -0.0142182 0.00250760   0.27879    -0.5484632    -0.21229502    -0.012128088
L_u[9,3]             -0.0093962 0.00249653   0.28988    -0.5625705    -0.21389277    -0.008372280
L_u[9,4]              0.0020518 0.00242878   0.28715    -0.5547114    -0.19743645     0.003038669
L_u[9,5]             -0.0098456 0.00233232   0.28797    -0.5568942    -0.21547740    -0.011699018
L_u[9,6]              0.0285579 0.00254097   0.28480    -0.5219290    -0.17183222     0.032007504
                            75%        97.5% n_eff   Rhat
beta[1]               5.7436262     5.799604  1470 1.0003
beta[2]               0.1448759     0.176214  8008 0.9996
beta[3]               0.0046509     0.035814 11033 1.0001
beta[4]              -0.0250399     0.026628  3717 1.0013
beta[5]              -0.0836334    -0.030294  4021 1.0009
beta[6]               0.0267339     0.055618  9957 0.9996
beta[7]              -0.0104658     0.018842 10482 1.0000
beta[8]               0.0210333     0.072646 12087 0.9997
beta[9]               0.0048688     0.054204 12517 0.9997
sigma_e               0.3968681     0.406687  8750 0.9997
sigma_u[1]            0.2721241     0.323789  1810 1.0018
sigma_u[2]            0.1258091     0.166874  2060 1.0017
sigma_u[3]            0.0303824     0.061450  7221 1.0000
sigma_u[4]            0.0382020     0.073839  4964 1.0011
sigma_u[5]            0.0539737     0.094945  3603 1.0012
sigma_u[6]            0.0577973     0.095719  3358 1.0008
sigma_u[7]            0.0421030     0.079998  4249 1.0008
sigma_u[8]            0.1013385     0.175856  3688 1.0002
sigma_u[9]            0.0726753     0.140210  5242 1.0000
sigma_w[1]            0.1323689     0.157345  3104 1.0003
sigma_w[2]            0.0645675     0.107832  3406 1.0002
sigma_w[3]            0.1175534     0.159070  1893 1.0013
L_u[1,1]              1.0000000     1.000000   NaN    NaN
L_u[1,2]              0.0000000     0.000000   NaN    NaN
L_u[1,3]              0.0000000     0.000000   NaN    NaN
L_u[1,4]              0.0000000     0.000000   NaN    NaN
L_u[1,5]              0.0000000     0.000000   NaN    NaN
L_u[1,6]              0.0000000     0.000000   NaN    NaN
L_u[1,7]              0.0000000     0.000000   NaN    NaN
L_u[1,8]              0.0000000     0.000000   NaN    NaN
L_u[1,9]              0.0000000     0.000000   NaN    NaN
L_u[2,1]              0.2944338     0.530669  8602 0.9999
L_u[2,2]              0.9963201     0.999966  5740 0.9998
L_u[2,3]              0.0000000     0.000000   NaN    NaN
L_u[2,4]              0.0000000     0.000000   NaN    NaN
L_u[2,5]              0.0000000     0.000000   NaN    NaN
L_u[2,6]              0.0000000     0.000000   NaN    NaN
L_u[2,7]              0.0000000     0.000000   NaN    NaN
L_u[2,8]              0.0000000     0.000000   NaN    NaN
L_u[2,9]              0.0000000     0.000000   NaN    NaN
L_u[3,1]              0.1630931     0.519153 17252 0.9997
L_u[3,2]              0.2290743     0.567851 17284 0.9999
L_u[3,3]              0.9724391     0.997542  2777 1.0012
L_u[3,4]              0.0000000     0.000000   NaN    NaN
L_u[3,5]              0.0000000     0.000000   NaN    NaN
L_u[3,6]              0.0000000     0.000000   NaN    NaN
L_u[3,7]              0.0000000     0.000000   NaN    NaN
L_u[3,8]              0.0000000     0.000000   NaN    NaN
L_u[3,9]              0.0000000     0.000000   NaN    NaN
L_u[4,1]              0.1756006     0.525587 14672 0.9997
L_u[4,2]              0.2095263     0.541930 15908 0.9998
L_u[4,3]              0.2014094     0.548018 15173 0.9998
L_u[4,4]              0.9417143     0.990184  3336 1.0014
L_u[4,5]              0.0000000     0.000000   NaN    NaN
L_u[4,6]              0.0000000     0.000000   NaN    NaN
L_u[4,7]              0.0000000     0.000000   NaN    NaN
L_u[4,8]              0.0000000     0.000000   NaN    NaN
L_u[4,9]              0.0000000     0.000000   NaN    NaN
L_u[5,1]              0.1442983     0.482115 14066 0.9996
L_u[5,2]              0.1927303     0.520266 12229 0.9998
L_u[5,3]              0.1934844     0.552053 10107 0.9999
L_u[5,4]              0.1971026     0.546213  9393 0.9998
L_u[5,5]              0.9053139     0.975247  4019 1.0011
L_u[5,6]              0.0000000     0.000000   NaN    NaN
L_u[5,7]              0.0000000     0.000000   NaN    NaN
L_u[5,8]              0.0000000     0.000000   NaN    NaN
L_u[5,9]              0.0000000     0.000000   NaN    NaN
L_u[6,1]              0.3670603     0.638058 12296 0.9997
L_u[6,2]              0.0676310     0.456796  9298 0.9996
L_u[6,3]              0.1970512     0.538322 10449 0.9997
L_u[6,4]              0.2023692     0.545344 10770 0.9998
L_u[6,5]              0.1868490     0.533895 10097 1.0000
L_u[6,6]              0.8422310     0.945662  3876 1.0002
L_u[6,7]              0.0000000     0.000000   NaN    NaN
L_u[6,8]              0.0000000     0.000000   NaN    NaN
L_u[6,9]              0.0000000     0.000000   NaN    NaN
L_u[7,1]              0.2159248     0.542306 13882 0.9998
L_u[7,2]              0.1828129     0.539232 13190 0.9997
L_u[7,3]              0.1994012     0.561794 13258 1.0000
L_u[7,4]              0.1858244     0.548850 13738 1.0004
L_u[7,5]              0.2011992     0.556384 12590 0.9999
L_u[7,6]              0.1578710     0.521280 12688 0.9996
L_u[7,7]              0.7992628     0.921234  3152 1.0006
L_u[7,8]              0.0000000     0.000000   NaN    NaN
L_u[7,9]              0.0000000     0.000000   NaN    NaN
L_u[8,1]              0.1654984     0.495741 18111 0.9999
L_u[8,2]              0.3357437     0.630349 10912 0.9996
L_u[8,3]              0.1925911     0.550140 10522 0.9999
L_u[8,4]              0.1899936     0.551566 10815 0.9997
L_u[8,5]              0.2381390     0.570667 10224 0.9996
L_u[8,6]              0.1454781     0.514621  9637 0.9998
L_u[8,7]              0.2009850     0.547276 10458 1.0004
L_u[8,8]              0.7395249     0.885437  4200 1.0004
L_u[8,9]              0.0000000     0.000000   NaN    NaN
L_u[9,1]              0.3205461     0.624845 16572 0.9996
L_u[9,2]              0.1779917     0.523988 12361 0.9998
L_u[9,3]              0.1999384     0.543844 13482 0.9999
L_u[9,4]              0.2034316     0.545934 13978 0.9996
L_u[9,5]              0.1963436     0.541883 15245 0.9998
L_u[9,6]              0.2311829     0.568936 12563 0.9996
 [ reached getOption("max.print") -- omitted 2782 rows ]

$c_summary
, , chains = chain:1

                   stats
parameter                     mean         sd           2.5%            25%            50%           75%
  beta[1]               5.71313764   0.043766     5.62739684     5.68300285     5.71254219     5.7431008
  beta[2]               0.12791934   0.024950     0.07498792     0.11203706     0.12819914     0.1451459
  beta[3]              -0.01112213   0.023176    -0.05676759    -0.02666853    -0.01110475     0.0036782
  beta[4]              -0.04960346   0.039123    -0.12849671    -0.07545078    -0.04963835    -0.0238676
  beta[5]              -0.11217025   0.040447    -0.18641035    -0.14051785    -0.11348946    -0.0852989
  beta[6]               0.01161017   0.022350    -0.03313501    -0.00338282     0.01135946     0.0266355
  beta[7]              -0.02447366   0.021900    -0.06722754    -0.03915830    -0.02470305    -0.0099852
  beta[8]              -0.00462087   0.037926    -0.07997108    -0.03014442    -0.00499958     0.0209451
  beta[9]              -0.02058520   0.036898    -0.09351357    -0.04481068    -0.02012587     0.0039586
  sigma_e               0.39207544   0.007006     0.37895110     0.38729379     0.39194511     0.3966504
  sigma_u[1]            0.25341894   0.031375     0.19717351     0.23161912     0.25047438     0.2727886
  sigma_u[2]            0.10676070   0.032429     0.03871591     0.08727580     0.10591218     0.1271076
  sigma_u[3]            0.02093646   0.016634     0.00092584     0.00786386     0.01684107     0.0301250
  sigma_u[4]            0.02562677   0.019635     0.00120941     0.01004256     0.02144569     0.0369157
  sigma_u[5]            0.03828180   0.026508     0.00150926     0.01655898     0.03422369     0.0559599
  sigma_u[6]            0.04100796   0.026239     0.00217504     0.01966322     0.03848443     0.0588815
  sigma_u[7]            0.02906976   0.022120     0.00086479     0.01126423     0.02495456     0.0421510
  sigma_u[8]            0.07094983   0.047754     0.00369507     0.03224510     0.06500220     0.1023060
  sigma_u[9]            0.05067299   0.037773     0.00215575     0.02084686     0.04349936     0.0727264
  sigma_w[1]            0.12096332   0.017302     0.09114024     0.10883240     0.11981586     0.1318524
  sigma_w[2]            0.04583392   0.029175     0.00206103     0.02178636     0.04395741     0.0649803
  sigma_w[3]            0.09328626   0.034371     0.02019346     0.07267751     0.09430597     0.1161593
  L_u[1,1]              1.00000000   0.000000     1.00000000     1.00000000     1.00000000     1.0000000
  L_u[1,2]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[1,3]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[1,4]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[1,5]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[1,6]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[1,7]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[1,8]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[1,9]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[2,1]              0.15691221   0.198403    -0.24633552     0.02057949     0.16170754     0.2921020
  L_u[2,2]              0.96650224   0.043690     0.83861601     0.95379683     0.98293134     0.9962636
  L_u[2,3]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[2,4]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[2,5]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[2,6]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[2,7]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[2,8]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[2,9]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[3,1]             -0.03815958   0.280535    -0.57738854    -0.23391465    -0.04326842     0.1580817
  L_u[3,2]              0.02327923   0.287158    -0.54561289    -0.18194694     0.02724731     0.2305909
  L_u[3,3]              0.91135128   0.079781     0.70215181     0.87428239     0.93284365     0.9724314
  L_u[3,4]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[3,5]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[3,6]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[3,7]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[3,8]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[3,9]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[4,1]             -0.01088570   0.274922    -0.52293787    -0.20278968    -0.01841786     0.1809470
  L_u[4,2]              0.00237729   0.286602    -0.55596744    -0.20295404     0.00247701     0.2057367
  L_u[4,3]             -0.00613867   0.282614    -0.54812308    -0.20956123    -0.01044375     0.1962542
  L_u[4,4]              0.86780109   0.096383     0.62852262     0.81271087     0.89242124     0.9401862
  L_u[4,5]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[4,6]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[4,7]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[4,8]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[4,9]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[5,1]             -0.03311159   0.263181    -0.52491811    -0.21878930    -0.04167821     0.1418992
  L_u[5,2]             -0.00799661   0.277738    -0.52568994    -0.21851840    -0.01131838     0.1935469
  L_u[5,3]             -0.01144692   0.287777    -0.54902437    -0.22284774    -0.01082654     0.2035603
  L_u[5,4]             -0.00584091   0.278193    -0.53964344    -0.20364515    -0.01745674     0.1892421
  L_u[5,5]              0.82493006   0.108234     0.55566337     0.76283284     0.84416201     0.9079595
  L_u[5,6]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[5,7]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[5,8]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[5,9]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[6,1]              0.16956210   0.267689    -0.37672018    -0.01220813     0.18580735     0.3721858
  L_u[6,2]             -0.12543769   0.282335    -0.64196700    -0.32807746    -0.13682650     0.0685792
  L_u[6,3]             -0.00459167   0.284918    -0.54725398    -0.20795335    -0.00874155     0.1976958
  L_u[6,4]              0.00057064   0.278315    -0.52223829    -0.20129768     0.00809006     0.1982392
  L_u[6,5]             -0.01470836   0.281455    -0.52534619    -0.21710368    -0.02559055     0.1899161
  L_u[6,6]              0.74015935   0.135720     0.44082532     0.65320809     0.75746274     0.8435301
  L_u[6,7]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[6,8]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[6,9]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[7,1]              0.02223804   0.286393    -0.54361721    -0.17311508     0.01798013     0.2284809
  L_u[7,2]             -0.02273791   0.284678    -0.54475863    -0.23416284    -0.03320993     0.1806743
  L_u[7,3]              0.00483081   0.290280    -0.54724821    -0.20213406     0.00165665     0.2087496
  L_u[7,4]             -0.00923222   0.286111    -0.54747484    -0.20349797    -0.00614642     0.1895690
  L_u[7,5]             -0.00262984   0.287465    -0.55387225    -0.20618379    -0.00880802     0.2087780
  L_u[7,6]             -0.05041789   0.296039    -0.61400879    -0.26902257    -0.04725425     0.1596983
  L_u[7,7]              0.69089174   0.140711     0.39227860     0.59995325     0.70340152     0.7983615
  L_u[7,8]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[7,9]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[8,1]             -0.01285382   0.261531    -0.53211650    -0.18674808    -0.01671620     0.1590671
  L_u[8,2]              0.12842828   0.273342    -0.42672633    -0.06064058     0.13308968     0.3284855
  L_u[8,3]             -0.01337892   0.293813    -0.56139709    -0.22236081    -0.01603426     0.1902357
  L_u[8,4]             -0.00745980   0.293162    -0.57102597    -0.22307292    -0.00792881     0.1930580
  L_u[8,5]              0.03277845   0.284400    -0.53456405    -0.16355298     0.03928224     0.2337294
  L_u[8,6]             -0.06042370   0.280563    -0.56892506    -0.26402724    -0.06592505     0.1350431
  L_u[8,7]             -0.00677262   0.294891    -0.59025188    -0.21309697     0.00582087     0.2015930
  L_u[8,8]              0.62771498   0.150523     0.32329845     0.51563223     0.63997883     0.7387466
  L_u[8,9]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_u[9,1]              0.10686680   0.293106    -0.48405733    -0.09298402     0.11863457     0.3227717
  L_u[9,2]             -0.01084245   0.282857    -0.53992432    -0.21696212    -0.00391492     0.1835020
  L_u[9,3]             -0.00891006   0.291879    -0.54734544    -0.21832521    -0.01351539     0.2004057
  L_u[9,4]              0.00069897   0.287099    -0.54085698    -0.20163435    -0.00395903     0.2065248
  L_u[9,5]             -0.01175079   0.284262    -0.55028206    -0.20846445    -0.01642432     0.1832899
  L_u[9,6]              0.02509193   0.283468    -0.52129200    -0.17244766     0.02640681     0.2266400
  L_u[9,7]             -0.00357695   0.297996    -0.58317961    -0.21948714     0.00082669     0.2097211
  L_u[9,8]             -0.01473812   0.286922    -0.57422429    -0.21179191    -0.01993437     0.1876050
  L_u[9,9]              0.54346818   0.162940     0.21563928     0.43077542     0.55047447     0.6643300
  L_w[1,1]              1.00000000   0.000000     1.00000000     1.00000000     1.00000000     1.0000000
  L_w[1,2]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_w[1,3]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_w[2,1]             -0.30401238   0.327826    -0.81357081    -0.54842820    -0.34794190    -0.1036936
  L_w[2,2]              0.88657918   0.118932     0.58143246     0.82790983     0.92729917     0.9779800
  L_w[2,3]              0.00000000   0.000000     0.00000000     0.00000000     0.00000000     0.0000000
  L_w[3,1]              0.09866434   0.284537    -0.49200055    -0.09506462     0.11150080     0.2962814
  L_w[3,2]              0.06968991   0.416688    -0.74774712    -0.23383831     0.08445145     0.3885910
  L_w[3,3]              0.84291921   0.142981     0.46238734     0.77594717     0.88659776     0.9503859
  z_u[1,1]             -0.58836305   0.281761    -1.16452379    -0.77433173    -0.59517008    -0.3933977
  z_u[1,2]             -0.19810587   0.760095    -1.73080437    -0.70680533    -0.20447592     0.2908181
  z_u[1,3]              0.05451835   0.965841    -1.81830564    -0.63292057     0.06102908     0.7309231
  z_u[1,4]             -0.06290138   0.946548    -1.85754943    -0.71735266    -0.06254379     0.5815496
  z_u[1,5]             -0.02408568   0.988924    -2.00436622    -0.68003504    -0.02460154     0.6140643
  z_u[1,6]             -0.03081845   0.971008    -1.95086607    -0.65307566    -0.03438576     0.5670417
  z_u[1,7]              0.00815684   0.978331    -1.86808703    -0.67652457    -0.00754877     0.6877695
  z_u[1,8]             -0.16442090   0.976689    -2.09053011    -0.85709358    -0.16786003     0.5090049
  z_u[1,9]             -0.12258556   0.980552    -1.99227901    -0.78296841    -0.13381940     0.5390079
  z_u[2,1]             -0.85130600   0.311091    -1.46355826    -1.05503372    -0.84438152    -0.6335456
  z_u[2,2]              0.41276530   0.774793    -1.17670203    -0.07002896     0.41045513     0.9031758
  z_u[2,3]             -0.09570116   0.993574    -2.00761788    -0.80007892    -0.08334085     0.5836216
  z_u[2,4]             -0.09015756   0.943493    -1.90553870    -0.74319352    -0.09933205     0.5593682
  z_u[2,5]              0.02767682   0.935284    -1.85522397    -0.63726149     0.03898061     0.6269704
  z_u[2,6]             -0.17627449   0.942760    -1.94560330    -0.80124044    -0.19876300     0.4672770
  z_u[2,7]              0.01783762   0.956114    -1.88396448    -0.60129251     0.03179733     0.6150744
  z_u[2,8]             -0.07304053   0.957596    -1.88858379    -0.72882270    -0.06598202     0.5688490
  z_u[2,9]              0.04326736   1.009585    -1.88559130    -0.65605537     0.02488998     0.7591116
  z_u[3,1]              0.25445523   0.273022    -0.29203265     0.07002352     0.25122544     0.4317084
  z_u[3,2]             -0.13891772   0.777111    -1.66120185    -0.66336185    -0.14096350     0.3946285
  z_u[3,3]              0.23215544   1.022291    -1.79390399    -0.47038038     0.22885645     0.9363247
  z_u[3,4]              0.05659148   0.961902    -1.81698240    -0.56878359     0.05211163     0.7230671
  z_u[3,5]             -0.35135651   1.020955    -2.34443886    -1.01799987    -0.34544883     0.3181704
  z_u[3,6]             -0.27131848   0.982458    -2.24011411    -0.92586297    -0.27164009     0.4090866
  z_u[3,7]              0.21244089   0.967088    -1.69067176    -0.44610883     0.20609954     0.8708612
  z_u[3,8]             -0.10754544   0.985513    -2.03641887    -0.78073117    -0.09562434     0.5852269
  z_u[3,9]             -0.10414296   0.996398    -2.04179143    -0.75452794    -0.10650734     0.5510401
  z_u[4,1]              0.96171463   0.293592     0.38699449     0.76344241     0.96441645     1.1553424
  z_u[4,2]              0.65782315   0.783633    -0.91940016     0.12884452     0.67974249     1.1961863
  z_u[4,3]             -0.16766907   0.967897    -2.09135456    -0.84537292    -0.18184419     0.5307259
                   stats
parameter                   97.5%
  beta[1]               5.8011956
  beta[2]               0.1759481
  beta[3]               0.0348597
  beta[4]               0.0272101
  beta[5]              -0.0275612
  beta[6]               0.0541748
  beta[7]               0.0200544
  beta[8]               0.0709304
  beta[9]               0.0535641
  sigma_e               0.4055103
  sigma_u[1]            0.3225137
  sigma_u[2]            0.1694784
  sigma_u[3]            0.0617176
  sigma_u[4]            0.0735970
  sigma_u[5]            0.0972911
  sigma_u[6]            0.0975369
  sigma_u[7]            0.0812909
  sigma_u[8]            0.1732239
  sigma_u[9]            0.1417590
  sigma_w[1]            0.1575509
  sigma_w[2]            0.1074995
  sigma_w[3]            0.1590697
  L_u[1,1]              1.0000000
  L_u[1,2]              0.0000000
  L_u[1,3]              0.0000000
  L_u[1,4]              0.0000000
  L_u[1,5]              0.0000000
  L_u[1,6]              0.0000000
  L_u[1,7]              0.0000000
  L_u[1,8]              0.0000000
  L_u[1,9]              0.0000000
  L_u[2,1]              0.5447230
  L_u[2,2]              0.9999672
  L_u[2,3]              0.0000000
  L_u[2,4]              0.0000000
  L_u[2,5]              0.0000000
  L_u[2,6]              0.0000000
  L_u[2,7]              0.0000000
  L_u[2,8]              0.0000000
  L_u[2,9]              0.0000000
  L_u[3,1]              0.5158774
  L_u[3,2]              0.5456324
  L_u[3,3]              0.9978860
  L_u[3,4]              0.0000000
  L_u[3,5]              0.0000000
  L_u[3,6]              0.0000000
  L_u[3,7]              0.0000000
  L_u[3,8]              0.0000000
  L_u[3,9]              0.0000000
  L_u[4,1]              0.5196079
  L_u[4,2]              0.5413704
  L_u[4,3]              0.5525111
  L_u[4,4]              0.9898307
  L_u[4,5]              0.0000000
  L_u[4,6]              0.0000000
  L_u[4,7]              0.0000000
  L_u[4,8]              0.0000000
  L_u[4,9]              0.0000000
  L_u[5,1]              0.4876544
  L_u[5,2]              0.5105310
  L_u[5,3]              0.5324791
  L_u[5,4]              0.5282536
  L_u[5,5]              0.9744629
  L_u[5,6]              0.0000000
  L_u[5,7]              0.0000000
  L_u[5,8]              0.0000000
  L_u[5,9]              0.0000000
  L_u[6,1]              0.6351133
  L_u[6,2]              0.4436601
  L_u[6,3]              0.5429731
  L_u[6,4]              0.5391719
  L_u[6,5]              0.5487482
  L_u[6,6]              0.9428585
  L_u[6,7]              0.0000000
  L_u[6,8]              0.0000000
  L_u[6,9]              0.0000000
  L_u[7,1]              0.5562617
  L_u[7,2]              0.5348565
  L_u[7,3]              0.5514739
  L_u[7,4]              0.5446926
  L_u[7,5]              0.5476183
  L_u[7,6]              0.5097937
  L_u[7,7]              0.9153556
  L_u[7,8]              0.0000000
  L_u[7,9]              0.0000000
  L_u[8,1]              0.4944285
  L_u[8,2]              0.6159061
  L_u[8,3]              0.5535389
  L_u[8,4]              0.5561478
  L_u[8,5]              0.5909508
  L_u[8,6]              0.4867742
  L_u[8,7]              0.5516640
  L_u[8,8]              0.8887552
  L_u[8,9]              0.0000000
  L_u[9,1]              0.6442499
  L_u[9,2]              0.5298522
  L_u[9,3]              0.5422653
  L_u[9,4]              0.5446919
  L_u[9,5]              0.5491093
  L_u[9,6]              0.5715091
  L_u[9,7]              0.5583302
  L_u[9,8]              0.5275687
  L_u[9,9]              0.8357232
  L_w[1,1]              1.0000000
  L_w[1,2]              0.0000000
  L_w[1,3]              0.0000000
  L_w[2,1]              0.4571604
  L_w[2,2]              0.9998347
  L_w[2,3]              0.0000000
  L_w[3,1]              0.6215726
  L_w[3,2]              0.7886976
  L_w[3,3]              0.9956475
  z_u[1,1]             -0.0535598
  z_u[1,2]              1.3277736
  z_u[1,3]              1.8427887
  z_u[1,4]              1.7374492
  z_u[1,5]              1.9529460
  z_u[1,6]              1.9636886
  z_u[1,7]              1.8837358
  z_u[1,8]              1.7373880
  z_u[1,9]              1.8479007
  z_u[2,1]             -0.2787172
  z_u[2,2]              1.9232673
  z_u[2,3]              1.8763920
  z_u[2,4]              1.7435384
  z_u[2,5]              1.8946007
  z_u[2,6]              1.6691390
  z_u[2,7]              1.9405676
  z_u[2,8]              1.7928960
  z_u[2,9]              2.0283817
  z_u[3,1]              0.8041124
  z_u[3,2]              1.3496413
  z_u[3,3]              2.1776256
  z_u[3,4]              1.9222296
  z_u[3,5]              1.7465426
  z_u[3,6]              1.6077870
  z_u[3,7]              2.1181724
  z_u[3,8]              1.8141622
  z_u[3,9]              1.8201266
  z_u[4,1]              1.5651933
  z_u[4,2]              2.1107694
  z_u[4,3]              1.6024478

 [ reached getOption("max.print") -- omitted 2740 row(s) and 3 matrix slice(s) ]
# check params
summary(m1_gd, pars = c('beta[1]', 'beta[2]', 'beta[3]', 'beta[4]', 'beta[5]', 'beta[6]', 'beta[7]', 'beta[8]', 'beta[9]'))
$summary
             mean   se_mean      sd     2.5%       25%       50%       75%    97.5% n_eff   Rhat
beta[1]  5.713728 0.0011603 0.04449  5.62554  5.684340  5.713521  5.743626  5.79960  1470 1.0003
beta[2]  0.127819 0.0002814 0.02518  0.07672  0.111494  0.127758  0.144876  0.17621  8008 0.9996
beta[3] -0.011082 0.0002263 0.02377 -0.05825 -0.026802 -0.011387  0.004651  0.03581 11033 1.0001
beta[4] -0.051262 0.0006498 0.03962 -0.13019 -0.077218 -0.051252 -0.025040  0.02663  3717 1.0013
beta[5] -0.110649 0.0006325 0.04011 -0.18656 -0.138336 -0.111422 -0.083633 -0.03029  4021 1.0009
beta[6]  0.011734 0.0002219 0.02214 -0.03127 -0.003099  0.011363  0.026734  0.05562  9957 0.9996
beta[7] -0.024834 0.0002134 0.02185 -0.06698 -0.039378 -0.024796 -0.010466  0.01884 10482 1.0000
beta[8] -0.004565 0.0003525 0.03875 -0.08067 -0.030323 -0.004716  0.021033  0.07265 12087 0.9997
beta[9] -0.020618 0.0003378 0.03779 -0.09544 -0.045598 -0.020266  0.004869  0.05420 12517 0.9997

$c_summary
, , chains = chain:1

         stats
parameter      mean      sd     2.5%       25%      50%       75%    97.5%
  beta[1]  5.713138 0.04377  5.62740  5.683003  5.71254  5.743101  5.80120
  beta[2]  0.127919 0.02495  0.07499  0.112037  0.12820  0.145146  0.17595
  beta[3] -0.011122 0.02318 -0.05677 -0.026669 -0.01110  0.003678  0.03486
  beta[4] -0.049603 0.03912 -0.12850 -0.075451 -0.04964 -0.023868  0.02721
  beta[5] -0.112170 0.04045 -0.18641 -0.140518 -0.11349 -0.085299 -0.02756
  beta[6]  0.011610 0.02235 -0.03314 -0.003383  0.01136  0.026635  0.05417
  beta[7] -0.024474 0.02190 -0.06723 -0.039158 -0.02470 -0.009985  0.02005
  beta[8] -0.004621 0.03793 -0.07997 -0.030144 -0.00500  0.020945  0.07093
  beta[9] -0.020585 0.03690 -0.09351 -0.044811 -0.02013  0.003959  0.05356

, , chains = chain:2

         stats
parameter      mean      sd     2.5%       25%       50%       75%    97.5%
  beta[1]  5.715037 0.04484  5.62636  5.685916  5.715053  5.745564  5.80027
  beta[2]  0.127851 0.02466  0.07754  0.112097  0.127127  0.144226  0.17510
  beta[3] -0.010676 0.02410 -0.05826 -0.025909 -0.011212  0.004843  0.03648
  beta[4] -0.051229 0.03913 -0.12902 -0.076160 -0.051931 -0.025282  0.02530
  beta[5] -0.111027 0.03969 -0.18941 -0.138046 -0.111284 -0.083035 -0.03563
  beta[6]  0.011793 0.02201 -0.03023 -0.003705  0.011121  0.026480  0.05697
  beta[7] -0.024973 0.02246 -0.06628 -0.039961 -0.025135 -0.010662  0.02017
  beta[8] -0.004396 0.03937 -0.08104 -0.030887 -0.003905  0.021598  0.07505
  beta[9] -0.020592 0.03804 -0.09312 -0.046980 -0.020486  0.005699  0.05219

, , chains = chain:3

         stats
parameter     mean      sd     2.5%       25%       50%       75%    97.5%
  beta[1]  5.71471 0.04561  5.62498  5.686380  5.714693  5.745015  5.80040
  beta[2]  0.12815 0.02577  0.07499  0.112058  0.128429  0.145906  0.17634
  beta[3] -0.01096 0.02410 -0.05808 -0.027102 -0.011103  0.005638  0.03571
  beta[4] -0.05028 0.04059 -0.12881 -0.077789 -0.050275 -0.022809  0.02851
  beta[5] -0.11035 0.03992 -0.18650 -0.136889 -0.110758 -0.084419 -0.02947
  beta[6]  0.01177 0.02219 -0.03032 -0.003241  0.011586  0.026975  0.05532
  beta[7] -0.02511 0.02162 -0.06740 -0.039077 -0.024977 -0.010970  0.01809
  beta[8] -0.00539 0.03962 -0.08210 -0.032134 -0.006221  0.021030  0.07272
  beta[9] -0.02108 0.03804 -0.09897 -0.046205 -0.020819  0.004461  0.05202

, , chains = chain:4

         stats
parameter      mean      sd     2.5%       25%       50%       75%    97.5%
  beta[1]  5.712026 0.04368  5.62513  5.682875  5.712404  5.740672  5.79525
  beta[2]  0.127359 0.02535  0.07959  0.109347  0.127041  0.143869  0.17762
  beta[3] -0.011570 0.02368 -0.05916 -0.027653 -0.011936  0.004601  0.03554
  beta[4] -0.053935 0.03951 -0.13240 -0.080639 -0.053332 -0.027947  0.02386
  beta[5] -0.109051 0.04033 -0.18296 -0.137600 -0.109879 -0.081789 -0.02636
  beta[6]  0.011759 0.02205 -0.03076 -0.002476  0.011476  0.026602  0.05549
  beta[7] -0.024785 0.02142 -0.06650 -0.039542 -0.024316 -0.010204  0.01554
  beta[8] -0.003853 0.03808 -0.07760 -0.028500 -0.004147  0.020882  0.07188
  beta[9] -0.020212 0.03820 -0.09636 -0.044417 -0.019729  0.005767  0.05676
# check convergence
traceplot(m1_gd, pars = c("beta"))


# check predicts --> posterior parameter distr.
y_posterior <- extract(m1_gd) 
y_posterior$beta[,1] #intercept
   [1] 5.751 5.751 5.679 5.755 5.769 5.735 5.704 5.784 5.787 5.658 5.768 5.707 5.673 5.663 5.671 5.740
  [17] 5.708 5.772 5.714 5.732 5.699 5.704 5.769 5.763 5.655 5.689 5.732 5.714 5.739 5.706 5.723 5.655
  [33] 5.773 5.697 5.695 5.676 5.726 5.752 5.669 5.665 5.715 5.734 5.594 5.739 5.707 5.711 5.747 5.695
  [49] 5.740 5.748 5.720 5.726 5.722 5.749 5.761 5.679 5.722 5.667 5.724 5.655 5.741 5.705 5.757 5.717
  [65] 5.704 5.630 5.604 5.754 5.710 5.710 5.645 5.677 5.760 5.705 5.679 5.775 5.707 5.771 5.724 5.699
  [81] 5.762 5.743 5.684 5.704 5.631 5.763 5.689 5.667 5.705 5.743 5.741 5.731 5.736 5.700 5.652 5.788
  [97] 5.747 5.753 5.703 5.705 5.803 5.691 5.667 5.658 5.699 5.650 5.733 5.702 5.685 5.748 5.788 5.733
 [113] 5.671 5.716 5.665 5.724 5.709 5.694 5.739 5.736 5.752 5.745 5.644 5.749 5.691 5.700 5.637 5.672
 [129] 5.735 5.697 5.701 5.713 5.712 5.759 5.709 5.651 5.697 5.757 5.691 5.743 5.705 5.731 5.699 5.659
 [145] 5.714 5.696 5.687 5.697 5.705 5.783 5.723 5.702 5.732 5.748 5.692 5.734 5.756 5.803 5.702 5.774
 [161] 5.805 5.757 5.728 5.696 5.702 5.677 5.678 5.686 5.676 5.700 5.681 5.716 5.703 5.714 5.737 5.685
 [177] 5.677 5.697 5.739 5.731 5.734 5.808 5.703 5.735 5.809 5.677 5.748 5.722 5.651 5.803 5.726 5.656
 [193] 5.668 5.727 5.767 5.820 5.742 5.733 5.624 5.719 5.668 5.663 5.769 5.761 5.768 5.790 5.709 5.692
 [209] 5.684 5.789 5.675 5.734 5.668 5.698 5.721 5.686 5.793 5.657 5.696 5.739 5.655 5.740 5.690 5.656
 [225] 5.727 5.694 5.718 5.704 5.694 5.698 5.722 5.733 5.665 5.726 5.742 5.704 5.684 5.681 5.736 5.643
 [241] 5.702 5.752 5.693 5.717 5.687 5.750 5.740 5.750 5.760 5.651 5.747 5.721 5.739 5.757 5.755 5.676
 [257] 5.730 5.767 5.654 5.786 5.701 5.735 5.664 5.691 5.742 5.741 5.699 5.715 5.673 5.711 5.714 5.787
 [273] 5.807 5.709 5.694 5.842 5.723 5.741 5.762 5.648 5.668 5.674 5.685 5.701 5.653 5.717 5.613 5.751
 [289] 5.754 5.691 5.694 5.757 5.763 5.621 5.683 5.728 5.643 5.679 5.681 5.719 5.668 5.743 5.723 5.684
 [305] 5.711 5.777 5.755 5.723 5.735 5.721 5.724 5.656 5.683 5.646 5.798 5.765 5.776 5.683 5.778 5.698
 [321] 5.697 5.607 5.779 5.649 5.703 5.665 5.632 5.686 5.782 5.703 5.715 5.686 5.715 5.716 5.715 5.738
 [337] 5.719 5.680 5.717 5.675 5.699 5.776 5.718 5.745 5.584 5.757 5.706 5.716 5.701 5.691 5.679 5.742
 [353] 5.662 5.680 5.717 5.769 5.698 5.750 5.701 5.740 5.835 5.702 5.717 5.730 5.754 5.651 5.740 5.685
 [369] 5.698 5.673 5.709 5.675 5.798 5.700 5.682 5.671 5.628 5.734 5.642 5.714 5.754 5.661 5.700 5.754
 [385] 5.726 5.818 5.761 5.733 5.696 5.712 5.654 5.701 5.782 5.668 5.689 5.734 5.737 5.750 5.796 5.695
 [401] 5.775 5.664 5.753 5.761 5.703 5.711 5.747 5.696 5.738 5.644 5.676 5.689 5.727 5.719 5.769 5.716
 [417] 5.719 5.683 5.664 5.727 5.671 5.695 5.758 5.736 5.754 5.738 5.704 5.629 5.729 5.677 5.677 5.691
 [433] 5.696 5.781 5.652 5.670 5.646 5.724 5.688 5.685 5.742 5.755 5.734 5.790 5.688 5.735 5.706 5.720
 [449] 5.751 5.629 5.732 5.655 5.798 5.718 5.660 5.735 5.753 5.739 5.708 5.710 5.746 5.672 5.703 5.700
 [465] 5.728 5.677 5.691 5.690 5.792 5.754 5.733 5.676 5.698 5.791 5.704 5.729 5.696 5.640 5.655 5.759
 [481] 5.676 5.684 5.670 5.719 5.699 5.712 5.678 5.649 5.715 5.677 5.733 5.692 5.664 5.755 5.664 5.624
 [497] 5.738 5.682 5.678 5.624 5.708 5.716 5.687 5.719 5.732 5.697 5.618 5.740 5.770 5.670 5.699 5.716
 [513] 5.699 5.743 5.741 5.745 5.757 5.671 5.721 5.709 5.737 5.649 5.777 5.665 5.687 5.709 5.688 5.709
 [529] 5.720 5.783 5.791 5.735 5.709 5.606 5.780 5.724 5.672 5.738 5.668 5.676 5.695 5.692 5.714 5.851
 [545] 5.772 5.764 5.686 5.673 5.753 5.678 5.752 5.727 5.714 5.683 5.635 5.727 5.673 5.713 5.786 5.681
 [561] 5.618 5.773 5.726 5.705 5.743 5.740 5.644 5.744 5.706 5.704 5.743 5.676 5.735 5.792 5.766 5.746
 [577] 5.754 5.616 5.759 5.680 5.712 5.667 5.681 5.681 5.782 5.725 5.698 5.709 5.696 5.762 5.713 5.692
 [593] 5.675 5.717 5.709 5.690 5.673 5.694 5.693 5.652 5.685 5.708 5.704 5.696 5.686 5.747 5.678 5.687
 [609] 5.737 5.700 5.752 5.715 5.756 5.650 5.767 5.747 5.707 5.747 5.712 5.690 5.719 5.731 5.671 5.708
 [625] 5.695 5.669 5.784 5.675 5.715 5.729 5.657 5.647 5.720 5.735 5.708 5.707 5.681 5.734 5.644 5.695
 [641] 5.673 5.708 5.685 5.722 5.701 5.671 5.759 5.688 5.688 5.698 5.766 5.801 5.710 5.701 5.775 5.716
 [657] 5.710 5.738 5.727 5.726 5.652 5.737 5.789 5.734 5.713 5.691 5.753 5.771 5.627 5.575 5.724 5.733
 [673] 5.697 5.707 5.762 5.720 5.696 5.656 5.769 5.728 5.689 5.765 5.690 5.762 5.694 5.673 5.691 5.706
 [689] 5.676 5.774 5.711 5.722 5.693 5.731 5.799 5.654 5.702 5.681 5.744 5.657 5.727 5.688 5.759 5.762
 [705] 5.753 5.718 5.816 5.624 5.739 5.647 5.682 5.729 5.713 5.723 5.673 5.770 5.618 5.725 5.746 5.717
 [721] 5.671 5.693 5.636 5.608 5.719 5.700 5.740 5.737 5.699 5.749 5.668 5.724 5.755 5.816 5.736 5.754
 [737] 5.712 5.721 5.715 5.667 5.757 5.704 5.718 5.738 5.778 5.693 5.772 5.668 5.721 5.703 5.708 5.675
 [753] 5.676 5.733 5.752 5.627 5.677 5.706 5.741 5.715 5.754 5.719 5.745 5.666 5.674 5.699 5.670 5.673
 [769] 5.740 5.689 5.661 5.709 5.681 5.715 5.730 5.675 5.713 5.770 5.727 5.650 5.722 5.720 5.757 5.710
 [785] 5.718 5.732 5.701 5.717 5.768 5.649 5.703 5.683 5.706 5.743 5.692 5.675 5.690 5.694 5.725 5.694
 [801] 5.710 5.731 5.778 5.720 5.781 5.727 5.709 5.732 5.694 5.740 5.658 5.744 5.745 5.738 5.820 5.699
 [817] 5.697 5.676 5.670 5.749 5.701 5.777 5.749 5.731 5.604 5.623 5.731 5.743 5.688 5.659 5.710 5.760
 [833] 5.758 5.765 5.713 5.711 5.636 5.731 5.652 5.631 5.664 5.640 5.756 5.658 5.763 5.627 5.637 5.711
 [849] 5.669 5.690 5.746 5.728 5.711 5.738 5.702 5.707 5.762 5.746 5.717 5.634 5.770 5.627 5.720 5.649
 [865] 5.748 5.770 5.760 5.765 5.760 5.799 5.688 5.677 5.757 5.747 5.687 5.647 5.731 5.619 5.645 5.758
 [881] 5.688 5.728 5.712 5.699 5.676 5.769 5.687 5.745 5.660 5.758 5.737 5.660 5.709 5.698 5.737 5.678
 [897] 5.785 5.707 5.791 5.746 5.683 5.663 5.648 5.708 5.723 5.675 5.704 5.709 5.701 5.677 5.652 5.705
 [913] 5.753 5.768 5.703 5.705 5.707 5.832 5.716 5.692 5.727 5.713 5.809 5.682 5.692 5.705 5.699 5.682
 [929] 5.701 5.624 5.671 5.684 5.799 5.660 5.656 5.656 5.788 5.819 5.693 5.758 5.724 5.723 5.714 5.637
 [945] 5.755 5.716 5.652 5.770 5.752 5.683 5.708 5.695 5.710 5.722 5.692 5.690 5.719 5.711 5.766 5.749
 [961] 5.779 5.692 5.726 5.731 5.746 5.694 5.766 5.753 5.675 5.764 5.640 5.672 5.659 5.658 5.777 5.690
 [977] 5.672 5.740 5.713 5.749 5.698 5.724 5.753 5.718 5.680 5.768 5.704 5.662 5.681 5.752 5.730 5.758
 [993] 5.661 5.747 5.744 5.603 5.741 5.778 5.674 5.789
 [ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,2] #Gram
   [1] 0.08617 0.14450 0.14781 0.12892 0.13129 0.13293 0.13507 0.14327 0.14966 0.14901 0.15392 0.14168
  [13] 0.14565 0.12623 0.15916 0.10220 0.11038 0.14037 0.13081 0.14557 0.14672 0.13640 0.09500 0.17139
  [25] 0.16874 0.15347 0.14150 0.14487 0.13399 0.16667 0.12961 0.09932 0.10611 0.11766 0.13663 0.13142
  [37] 0.13723 0.07287 0.12577 0.06899 0.08543 0.12287 0.14221 0.14713 0.14097 0.07892 0.15540 0.11159
  [49] 0.11691 0.10813 0.11586 0.14688 0.13697 0.13043 0.15569 0.14091 0.07121 0.12177 0.13269 0.10608
  [61] 0.14402 0.12714 0.14093 0.14937 0.11413 0.12640 0.09119 0.13837 0.11502 0.10160 0.11414 0.07872
  [73] 0.12658 0.12101 0.12752 0.11559 0.15402 0.14726 0.11283 0.13488 0.13121 0.18106 0.11671 0.15492
  [85] 0.13950 0.12340 0.08882 0.13593 0.16429 0.14245 0.13861 0.12153 0.09980 0.10797 0.09945 0.12834
  [97] 0.15143 0.12603 0.07650 0.13247 0.09456 0.15212 0.13793 0.13436 0.09781 0.15148 0.16614 0.11385
 [109] 0.13221 0.15689 0.09666 0.11132 0.15233 0.10938 0.09012 0.09036 0.10308 0.14140 0.09856 0.13506
 [121] 0.11320 0.11403 0.15441 0.10683 0.11736 0.14178 0.14920 0.12592 0.12665 0.11999 0.16340 0.15276
 [133] 0.11877 0.12594 0.15326 0.13985 0.16150 0.13362 0.08675 0.13806 0.11504 0.15282 0.08628 0.12197
 [145] 0.12630 0.12706 0.09020 0.13931 0.11416 0.09610 0.11782 0.13046 0.12236 0.10923 0.12507 0.10658
 [157] 0.10899 0.10802 0.14134 0.12987 0.12139 0.13758 0.16275 0.14353 0.12167 0.14724 0.10874 0.10913
 [169] 0.12759 0.11972 0.12935 0.09372 0.10615 0.11235 0.14458 0.10970 0.07592 0.10678 0.15926 0.13222
 [181] 0.09524 0.13321 0.14408 0.17678 0.13372 0.13564 0.17511 0.16764 0.12833 0.13570 0.10194 0.08344
 [193] 0.14541 0.05377 0.15002 0.14793 0.12174 0.15278 0.09622 0.12525 0.13503 0.11550 0.14684 0.15213
 [205] 0.10659 0.11622 0.11635 0.11295 0.16448 0.10423 0.12234 0.12843 0.14249 0.10947 0.14683 0.11571
 [217] 0.13161 0.13919 0.12992 0.13853 0.14603 0.13049 0.07034 0.10650 0.08632 0.14786 0.12386 0.17493
 [229] 0.15394 0.10624 0.14887 0.10067 0.12072 0.11424 0.15735 0.16042 0.13650 0.16613 0.17444 0.12783
 [241] 0.14970 0.14304 0.11044 0.15157 0.15409 0.12360 0.11551 0.14503 0.12144 0.15400 0.12469 0.14487
 [253] 0.13745 0.12516 0.15474 0.13063 0.13175 0.12857 0.12539 0.11606 0.12027 0.19170 0.14063 0.08966
 [265] 0.15662 0.18473 0.08527 0.15371 0.09278 0.13630 0.10137 0.16206 0.11499 0.10935 0.11129 0.13247
 [277] 0.09457 0.10138 0.12166 0.12270 0.14339 0.12127 0.11183 0.10896 0.13236 0.10664 0.15681 0.13962
 [289] 0.13195 0.11184 0.09870 0.13971 0.07197 0.16672 0.12538 0.16240 0.10174 0.09411 0.09811 0.13354
 [301] 0.12356 0.13345 0.13153 0.11092 0.14653 0.11313 0.11546 0.13909 0.09056 0.12839 0.12640 0.09912
 [313] 0.15115 0.19916 0.14901 0.11999 0.11155 0.11490 0.13676 0.09680 0.12861 0.07439 0.10428 0.13824
 [325] 0.14970 0.10536 0.14174 0.10326 0.12899 0.14187 0.14581 0.15519 0.11016 0.09563 0.10252 0.13397
 [337] 0.16051 0.12326 0.11764 0.14207 0.12551 0.11481 0.15527 0.10294 0.06382 0.15177 0.11655 0.14535
 [349] 0.11328 0.14300 0.14893 0.14658 0.14335 0.12075 0.11869 0.09880 0.13223 0.15161 0.12589 0.19733
 [361] 0.12428 0.13698 0.13464 0.14514 0.10326 0.10642 0.14590 0.15283 0.15162 0.10011 0.12288 0.16207
 [373] 0.11674 0.13134 0.09859 0.13174 0.10200 0.10511 0.11659 0.12234 0.15189 0.12752 0.12517 0.12137
 [385] 0.12267 0.04446 0.11596 0.13643 0.15836 0.14658 0.10246 0.16870 0.18755 0.16820 0.10961 0.18075
 [397] 0.11354 0.07171 0.09722 0.17022 0.11216 0.16280 0.15870 0.20582 0.14103 0.14484 0.17494 0.13771
 [409] 0.12974 0.15814 0.14417 0.14738 0.14563 0.08168 0.11137 0.15177 0.12507 0.11288 0.16363 0.10223
 [421] 0.14722 0.11968 0.15899 0.12449 0.13869 0.09205 0.13671 0.15691 0.11568 0.14063 0.12028 0.12888
 [433] 0.11979 0.06626 0.14543 0.08626 0.09647 0.07153 0.13870 0.13193 0.14451 0.14357 0.08044 0.16551
 [445] 0.13039 0.15365 0.08732 0.10992 0.12014 0.11644 0.14069 0.12645 0.14660 0.09043 0.09341 0.14937
 [457] 0.11532 0.14833 0.09702 0.12638 0.12350 0.11720 0.14513 0.11679 0.11915 0.12738 0.09644 0.13432
 [469] 0.10587 0.13283 0.10026 0.17041 0.11957 0.13881 0.12078 0.12541 0.11916 0.16420 0.09504 0.15381
 [481] 0.15325 0.11999 0.16943 0.08005 0.13699 0.08099 0.13111 0.06213 0.12644 0.10711 0.10377 0.16842
 [493] 0.10034 0.14969 0.12705 0.14769 0.15639 0.12379 0.10424 0.08496 0.13809 0.18324 0.13765 0.13690
 [505] 0.08981 0.09890 0.11781 0.11738 0.10540 0.09849 0.17603 0.13824 0.14696 0.18071 0.14536 0.12971
 [517] 0.12861 0.11966 0.11337 0.14504 0.09835 0.15846 0.09273 0.14971 0.17260 0.08591 0.13329 0.14821
 [529] 0.18201 0.15028 0.11969 0.17040 0.13395 0.13105 0.13440 0.15633 0.13406 0.04336 0.13434 0.12383
 [541] 0.14595 0.15120 0.13406 0.12190 0.10521 0.11391 0.14633 0.17315 0.14189 0.10951 0.16390 0.20014
 [553] 0.15735 0.12947 0.14806 0.11584 0.10904 0.11258 0.14033 0.17200 0.11461 0.13188 0.11179 0.09028
 [565] 0.13988 0.11073 0.12052 0.15197 0.11854 0.14750 0.13312 0.16275 0.14386 0.13822 0.12464 0.12385
 [577] 0.14302 0.10609 0.15688 0.11489 0.10273 0.11644 0.07321 0.13547 0.12621 0.13460 0.18187 0.15457
 [589] 0.08827 0.14831 0.13677 0.15855 0.13647 0.06721 0.10787 0.13113 0.12921 0.13534 0.11620 0.09270
 [601] 0.09484 0.09778 0.12848 0.14979 0.16408 0.11892 0.14428 0.12656 0.11207 0.10928 0.14767 0.08120
 [613] 0.12933 0.08151 0.13366 0.13624 0.18558 0.14420 0.14177 0.12611 0.06975 0.15571 0.13356 0.12182
 [625] 0.09578 0.11959 0.15866 0.08269 0.14066 0.10946 0.17298 0.10976 0.11781 0.13868 0.05977 0.13679
 [637] 0.12436 0.07208 0.13179 0.08501 0.11879 0.11218 0.13005 0.14813 0.11696 0.13584 0.11975 0.06660
 [649] 0.16091 0.11411 0.12108 0.13884 0.11474 0.12107 0.17435 0.12664 0.18112 0.13733 0.12864 0.16557
 [661] 0.09005 0.10089 0.11636 0.11584 0.12421 0.12055 0.13789 0.14543 0.10242 0.07911 0.09262 0.12197
 [673] 0.16440 0.14262 0.12237 0.11486 0.12937 0.14811 0.14572 0.14774 0.14459 0.12204 0.11245 0.14830
 [685] 0.09418 0.12415 0.12969 0.03976 0.13366 0.09318 0.18340 0.13630 0.13642 0.13825 0.12225 0.13120
 [697] 0.06837 0.11119 0.13583 0.15370 0.11017 0.13900 0.14234 0.12275 0.13802 0.12296 0.14886 0.13570
 [709] 0.11289 0.10159 0.16925 0.11048 0.12514 0.09460 0.14195 0.11614 0.13376 0.11309 0.08897 0.14748
 [721] 0.12434 0.12826 0.10744 0.15766 0.10934 0.14036 0.16213 0.08833 0.08198 0.18223 0.11762 0.13460
 [733] 0.14590 0.12241 0.09997 0.13471 0.15441 0.15375 0.12639 0.14780 0.12179 0.16359 0.09880 0.15218
 [745] 0.15039 0.14517 0.14172 0.17587 0.12947 0.13115 0.12604 0.10719 0.12735 0.12851 0.11255 0.12703
 [757] 0.13626 0.10706 0.13023 0.11124 0.15471 0.12989 0.11844 0.11613 0.12551 0.13302 0.16780 0.13311
 [769] 0.11801 0.12862 0.13812 0.12966 0.12752 0.14380 0.10379 0.09667 0.12599 0.13431 0.12687 0.11303
 [781] 0.12263 0.09326 0.10503 0.14186 0.13954 0.14169 0.14216 0.19075 0.12190 0.13115 0.11137 0.10626
 [793] 0.07689 0.07665 0.14223 0.11591 0.14908 0.13715 0.10665 0.15157 0.10092 0.16448 0.14414 0.10579
 [805] 0.14298 0.13891 0.13119 0.12187 0.16606 0.12191 0.13380 0.15242 0.14120 0.15015 0.12004 0.07321
 [817] 0.14435 0.18583 0.09775 0.13144 0.10317 0.10751 0.10747 0.12284 0.09342 0.11387 0.10395 0.13351
 [829] 0.18951 0.11376 0.10294 0.12265 0.15853 0.13155 0.14616 0.15013 0.09802 0.12476 0.14579 0.10173
 [841] 0.09113 0.10351 0.12155 0.11525 0.11997 0.12907 0.13051 0.16481 0.14488 0.10562 0.13479 0.13406
 [853] 0.11385 0.14188 0.16336 0.09901 0.11036 0.13098 0.14489 0.11368 0.10775 0.10988 0.17271 0.14885
 [865] 0.14082 0.11923 0.13267 0.11739 0.14694 0.12680 0.15773 0.08823 0.14693 0.14772 0.10690 0.11177
 [877] 0.14767 0.12014 0.08837 0.14243 0.12652 0.13006 0.11516 0.14220 0.12479 0.12474 0.13651 0.17067
 [889] 0.10511 0.10579 0.14242 0.07305 0.15001 0.17609 0.13973 0.11461 0.14059 0.09798 0.15346 0.11536
 [901] 0.10475 0.10956 0.15445 0.13200 0.10959 0.13826 0.11600 0.07504 0.13317 0.14467 0.15274 0.07781
 [913] 0.12626 0.13610 0.16437 0.11658 0.12503 0.11983 0.11008 0.11812 0.16014 0.11373 0.15979 0.16546
 [925] 0.15146 0.09879 0.07878 0.16929 0.07895 0.13191 0.14494 0.15776 0.13905 0.11267 0.10646 0.14932
 [937] 0.15015 0.15001 0.06972 0.16959 0.09255 0.09434 0.10668 0.14737 0.14571 0.13768 0.10172 0.13162
 [949] 0.12990 0.12523 0.14373 0.15639 0.14883 0.10322 0.10213 0.14080 0.13515 0.13795 0.16567 0.13562
 [961] 0.14240 0.11704 0.13071 0.10481 0.13580 0.08970 0.14595 0.12253 0.12586 0.16377 0.14675 0.15883
 [973] 0.10412 0.10696 0.07533 0.13344 0.06805 0.15634 0.11425 0.12240 0.13767 0.10120 0.14719 0.12186
 [985] 0.12799 0.16315 0.06677 0.11491 0.14141 0.17970 0.07001 0.14943 0.11737 0.16799 0.08687 0.06966
 [997] 0.14371 0.16850 0.13180 0.13108
 [ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,3] #Gen
   [1] -0.00106944  0.01723637 -0.00707412 -0.01223233 -0.00727736  0.03102150  0.00780335  0.01196708
   [9]  0.00060111 -0.00344965 -0.03276547  0.01570662 -0.03691361  0.01670390  0.00211250 -0.04958785
  [17]  0.02476726 -0.01359971  0.01159031  0.02364573  0.03294483  0.03331003 -0.00368468 -0.01160258
  [25] -0.03407884 -0.02242436 -0.01664567 -0.00280030 -0.03347919 -0.02060326 -0.05208339 -0.01961318
  [33] -0.02815123  0.00331478 -0.01284463 -0.01367274 -0.02595851 -0.01838227 -0.00002843 -0.03618458
  [41] -0.01911992  0.00769638 -0.02233802 -0.01631122 -0.03514461 -0.03382256 -0.04279913 -0.05639932
  [49] -0.00841101  0.02908361 -0.01428011 -0.03452012  0.00285543 -0.04276985  0.02530807  0.02828814
  [57] -0.01073406 -0.02072174 -0.01463113 -0.03408588  0.06491618 -0.04641248  0.00018524  0.01746849
  [65]  0.02410551 -0.02554732 -0.03213040 -0.03202411 -0.03309063 -0.04789999  0.01868149 -0.01800614
  [73] -0.07243857 -0.00036751 -0.00078828  0.01255899  0.01610201  0.02360909 -0.03459102  0.00979454
  [81] -0.03137738 -0.00196057 -0.03465186 -0.03683959 -0.00795662 -0.03537149 -0.01463421 -0.02080247
  [89] -0.02633286  0.01936775 -0.01110323 -0.03832089 -0.02237906 -0.02946058 -0.04860224 -0.00710820
  [97] -0.02651200 -0.01536221 -0.01209059 -0.01820591 -0.02934318  0.02189592 -0.03323197  0.01908551
 [105]  0.00734194 -0.00799459  0.00371379 -0.03003360 -0.03243708 -0.00525939 -0.03220836 -0.00280695
 [113]  0.01631044 -0.01961994 -0.03633019 -0.02685439 -0.01763903 -0.00592809 -0.00707304 -0.04679316
 [121]  0.00601756 -0.01155093 -0.00244633 -0.00423372  0.00627744 -0.00284373  0.01098382 -0.01582925
 [129] -0.01773085  0.01162967  0.01780629  0.01568926 -0.01706149  0.01936326 -0.01371813  0.01019764
 [137] -0.04970900  0.01175227 -0.00364758  0.00517922 -0.00252040 -0.02988482 -0.03316348  0.00363462
 [145] -0.00724080 -0.01502081 -0.00130272  0.00984024 -0.02423766 -0.02989626 -0.05469784  0.01299272
 [153] -0.00126771 -0.03893603  0.03084896  0.00541898  0.00169659 -0.02932019 -0.02668506 -0.02836923
 [161] -0.01196805  0.01924242 -0.01967250  0.00981299  0.00329113 -0.01166762 -0.03326474 -0.00786504
 [169] -0.01477161 -0.00342725  0.00522616 -0.04724767  0.02407782  0.00808069 -0.02637878 -0.03037316
 [177] -0.05359858 -0.02430850 -0.02586993 -0.03564347 -0.01689138 -0.01264732 -0.03084462  0.02797533
 [185] -0.04126085 -0.01275005 -0.01103491  0.01012007 -0.00796038  0.03335229  0.00607155  0.03412742
 [193]  0.01020440  0.00005119  0.03512643 -0.01002376 -0.02842485 -0.04169525 -0.02244722 -0.00388589
 [201]  0.00342806  0.00608732 -0.03242061  0.00029119 -0.01230875  0.05841400  0.00844215 -0.01079805
 [209] -0.03913770 -0.02528725  0.02791950  0.00358349 -0.00021585  0.02551071 -0.00942989  0.00568125
 [217] -0.02119962  0.00631696  0.02434835  0.02028426 -0.05079196 -0.02489043 -0.02409695 -0.02655619
 [225] -0.02232372 -0.02316131 -0.02351124 -0.00619559 -0.01888899 -0.03413848 -0.01931235  0.01975430
 [233] -0.02900788 -0.04202131  0.02240931 -0.02088645 -0.01760849 -0.05567408 -0.02926210 -0.06655290
 [241] -0.02245812 -0.03541059 -0.01808600  0.01946438  0.04481387  0.01558987  0.00997865 -0.00233513
 [249] -0.04220607 -0.00127102 -0.01164510 -0.02381192  0.04088900 -0.04206640 -0.01683782 -0.01657179
 [257]  0.00162341 -0.03766728 -0.01725767  0.06457781 -0.00709269  0.00489113 -0.00845587  0.00847901
 [265] -0.01197224  0.01932045 -0.01149348  0.03788221  0.01766121 -0.04309136  0.00028310  0.00003375
 [273] -0.00919706 -0.05949156 -0.00291287 -0.00706582  0.01257272 -0.01914974  0.00177092 -0.00595267
 [281]  0.02207284  0.00162421 -0.02896840 -0.06484883  0.03746989 -0.01045558  0.03116725  0.00982722
 [289] -0.03014796 -0.00983561 -0.00353908  0.02101640  0.00202344  0.00901520  0.01665022 -0.03458736
 [297] -0.02545730 -0.02842022  0.00386381 -0.04129583 -0.03258765 -0.03016905  0.00666764 -0.00822739
 [305]  0.02375268 -0.01667201 -0.01582260 -0.02617007 -0.02621151 -0.03164478 -0.02141022 -0.01359232
 [313]  0.01896960 -0.02062319 -0.00253318  0.01589958 -0.00132210 -0.00278092 -0.02283437 -0.01221491
 [321] -0.04634593  0.01435119 -0.00540659 -0.03972040 -0.00445768  0.00778914 -0.01705931 -0.03095313
 [329] -0.00925838 -0.00844864 -0.03830754 -0.03593573 -0.04070685 -0.02078757 -0.00508927 -0.02852232
 [337]  0.00658868 -0.02314283 -0.01290900 -0.03449931 -0.03447128 -0.01584038 -0.01826927 -0.00872635
 [345] -0.00259939 -0.02339660  0.00486272 -0.05098474 -0.01351135  0.00430683 -0.01654341  0.01790155
 [353]  0.00649948 -0.03737773  0.01984770  0.02065052  0.00011394 -0.00545146  0.01981835 -0.02157864
 [361] -0.01465082 -0.01822516 -0.05112824  0.00208944  0.03748515  0.00208152 -0.01956229  0.01233414
 [369] -0.04405349  0.00366630 -0.01195947 -0.03133940  0.03794244 -0.03524457 -0.01327844 -0.04865635
 [377]  0.02815125 -0.00660625 -0.00556524 -0.00046465 -0.02510306 -0.05670267  0.00698370 -0.01587887
 [385] -0.01552686 -0.00953153  0.03599736  0.01085109 -0.01905755 -0.00754857  0.00903336  0.02597144
 [393] -0.02077984 -0.02312637  0.00081366 -0.00888338  0.01781148 -0.01974338  0.00837198  0.02992075
 [401]  0.00016919 -0.01777196 -0.01686568 -0.02472697  0.01683596 -0.00115752 -0.00028660 -0.02589050
 [409] -0.03492567 -0.02522668 -0.00248249 -0.03172562  0.00920490 -0.04678724 -0.01257646  0.02196856
 [417]  0.00690270 -0.03185368 -0.03081723 -0.02520064  0.00496535 -0.05521860 -0.00479881 -0.06056265
 [425] -0.03258747  0.00099649 -0.05228077 -0.02108835 -0.00029120 -0.02813967 -0.04107349 -0.03174505
 [433] -0.02113880 -0.01816748  0.03238059  0.00803342  0.03766730  0.01776451 -0.02582345  0.00791820
 [441] -0.01096682  0.04238890 -0.00885886 -0.01110628  0.00027975 -0.03184796  0.01112439 -0.01299737
 [449] -0.03296547  0.01137316 -0.01462192  0.00325612 -0.01079400 -0.00676842 -0.01052409  0.03477417
 [457]  0.00921222  0.01885366 -0.01468075 -0.01808948 -0.02051672 -0.03028779 -0.01178273 -0.02387978
 [465] -0.02497720  0.01635957 -0.01762283 -0.05129534 -0.01551434 -0.04395930 -0.00972292 -0.03370353
 [473] -0.04263258 -0.05017151 -0.01715730 -0.05990051 -0.04053610 -0.01753843 -0.01544986  0.00373972
 [481] -0.05302933 -0.03952766 -0.01989581 -0.01054773 -0.03492909  0.00210697 -0.00374140 -0.02833263
 [489]  0.01370584 -0.04438683 -0.03012874  0.00233946 -0.03668764  0.01997901  0.00902279  0.02087751
 [497] -0.01892860 -0.01628297 -0.03802780  0.00176971  0.00210700 -0.06014376 -0.02507292 -0.00727020
 [505] -0.03372086  0.02196662 -0.00241072  0.00996763 -0.03282617 -0.01049957 -0.00560291 -0.03464997
 [513] -0.04086157 -0.01268797 -0.01035984 -0.03935688 -0.01542797  0.04062241 -0.01391082 -0.02681293
 [521]  0.00100328 -0.00631745 -0.01628477 -0.00546902  0.03568360  0.02363068 -0.02324745 -0.02622676
 [529] -0.01720064  0.01428435  0.01227005 -0.01677244 -0.03239156 -0.00693315  0.00098400  0.00572660
 [537] -0.01183543  0.01044649 -0.02488978  0.01333316 -0.03442890 -0.00438871 -0.00700452 -0.02462506
 [545]  0.01231491  0.03157708 -0.04024764 -0.00683572  0.00703481 -0.03982794  0.00301000  0.00773979
 [553] -0.00683832 -0.05775127  0.01513957 -0.02035579 -0.01687040 -0.03722332 -0.03409375 -0.03492758
 [561] -0.03924802 -0.03758798 -0.00606526 -0.02602327  0.00507645 -0.02023009 -0.01311032 -0.00857505
 [569] -0.01567366 -0.03882807  0.01846517 -0.03991775 -0.01024226 -0.02290371 -0.01998916 -0.01877028
 [577] -0.03044583  0.04748663  0.03546417 -0.00663156  0.00880826 -0.00973563 -0.01853473 -0.00619357
 [585]  0.00461061  0.04822562 -0.02162730  0.01028704  0.00990726  0.00205172 -0.03544957 -0.02711232
 [593] -0.05796819 -0.03307294 -0.05878819 -0.00919060 -0.01122971  0.00632304  0.00718582 -0.00067684
 [601] -0.02942251 -0.01564453 -0.02172695 -0.02362901  0.00516568 -0.01394422 -0.00242754 -0.02801939
 [609] -0.00309194 -0.02384221 -0.00436496  0.00883081 -0.00026676 -0.00315559 -0.00132654  0.01611153
 [617] -0.00832194 -0.02211942 -0.03257068  0.05218372  0.00183719 -0.01876673 -0.01588151 -0.00646693
 [625] -0.01427250 -0.03213229  0.00890484 -0.02481306 -0.00929065 -0.05987277  0.02428045  0.01711637
 [633]  0.00730879 -0.00892640 -0.00738414 -0.02997289  0.00979930 -0.00190055  0.00239534  0.00084795
 [641] -0.02193393  0.00099078 -0.01187751 -0.03648457 -0.05557616  0.00076893 -0.02145352 -0.02912481
 [649]  0.01433732 -0.00123894 -0.00004007  0.01225740  0.01117872 -0.00811510 -0.01604459  0.07323063
 [657] -0.04747832 -0.02410094 -0.02893800 -0.00701050  0.01995386  0.02005784  0.01314236 -0.02637196
 [665] -0.01619821 -0.02758845 -0.03565105 -0.02334607  0.00242444  0.00265449 -0.02034734  0.01083825
 [673] -0.02258150 -0.00504424  0.03422268  0.01223938 -0.00735531  0.04015122 -0.01997087 -0.02554533
 [681]  0.04312141  0.01520341 -0.04792968  0.05938690  0.01557169  0.00688373 -0.01566435 -0.00958298
 [689] -0.01041508 -0.00472088 -0.04064655 -0.00105047  0.00832744  0.00081818  0.02160404 -0.02580389
 [697] -0.00901546  0.00011406  0.00067579 -0.00326265 -0.01492123 -0.03929750 -0.01969733  0.04071537
 [705] -0.01357449  0.01363671 -0.02494684 -0.06536937 -0.00732026 -0.03834445  0.00863856 -0.02242512
 [713] -0.01224648 -0.03390430 -0.01496557 -0.02922993 -0.01208147  0.02468910 -0.05863531 -0.01225846
 [721] -0.02029693 -0.02045500 -0.02450003  0.02054137  0.01162721 -0.05112858 -0.00229661  0.01457119
 [729] -0.05279079 -0.01699768 -0.01982464  0.02441704  0.01849751 -0.04370533 -0.03389876  0.00291913
 [737] -0.01709731 -0.04704729 -0.01487041 -0.01589592 -0.01093381  0.00743658 -0.05033107  0.01113807
 [745]  0.01199589 -0.00869008 -0.03187038 -0.01490141  0.00431069 -0.00836946 -0.01933009 -0.01299569
 [753]  0.01939044 -0.01211408 -0.03175936  0.00104045 -0.06231909 -0.00704012  0.00121711 -0.03546202
 [761] -0.00837727  0.02803214 -0.02553158 -0.02298883 -0.03858549 -0.01014826  0.01495790  0.02354989
 [769]  0.02211439 -0.02566655 -0.00504674 -0.01101805 -0.00692982 -0.03921325 -0.03940566 -0.00749599
 [777]  0.03805929 -0.00620814 -0.00374396 -0.02325237  0.02929413 -0.01745231  0.00640088 -0.01962632
 [785] -0.04124297  0.01411610 -0.01072105 -0.04168620 -0.01401384 -0.03861442 -0.03007221 -0.02177622
 [793]  0.00216091 -0.01711472 -0.00183582 -0.01231198 -0.00530485 -0.02824534 -0.03609937  0.01446428
 [801] -0.03790871 -0.03364501 -0.00489917  0.00168372 -0.03390708 -0.01933169 -0.03085077 -0.02672656
 [809] -0.02074484 -0.00710802 -0.00882460  0.01411932 -0.08736411 -0.03426144 -0.00185984  0.01118655
 [817]  0.00415453 -0.00773135 -0.09541381 -0.00557229  0.00164094  0.00695491 -0.00686187  0.00213255
 [825] -0.02446441  0.00598593 -0.00113848 -0.03604497 -0.00185691 -0.01839547 -0.01518696 -0.01472526
 [833] -0.01685308 -0.01192389 -0.02214717 -0.04914391 -0.04079834  0.03539863 -0.00171740 -0.03380130
 [841] -0.02404942  0.01434104 -0.06482689 -0.07417141 -0.02516215 -0.00641454 -0.05277775  0.01234494
 [849] -0.00884694 -0.00240308  0.01558770 -0.00946331 -0.01591890 -0.00097933 -0.01222791  0.00500122
 [857] -0.03739292 -0.04379169  0.00310875 -0.00944391 -0.00369027 -0.03458154 -0.03487056 -0.00130277
 [865]  0.03485286 -0.01882366 -0.02385289 -0.01841777 -0.03952611 -0.01008472 -0.03851531 -0.00089775
 [873] -0.02232514 -0.00249789 -0.03292799 -0.00740002 -0.00287756  0.01614691 -0.01264950 -0.00181115
 [881] -0.00624689  0.00931476 -0.02494153 -0.01776656 -0.05625175 -0.02222642 -0.01655510 -0.00704593
 [889]  0.01894296 -0.00211182 -0.03411154  0.01283986 -0.04616431  0.00447970 -0.04503459 -0.01860260
 [897] -0.02639233 -0.00608991  0.02223530 -0.03133591 -0.03233147 -0.00416417 -0.00527246 -0.00423827
 [905]  0.04844717 -0.03769122 -0.00652885 -0.00360693  0.00549933 -0.01422108  0.01877131  0.00452042
 [913] -0.03397264 -0.01017858 -0.02275584 -0.01395020 -0.00722969 -0.00690616  0.01087840 -0.02097252
 [921] -0.00807237 -0.00529911  0.02903791 -0.02359836  0.00430918  0.01954801  0.03255103  0.00481096
 [929]  0.01222095 -0.01531878 -0.07589472 -0.00283592 -0.00971636 -0.02628842 -0.02630826 -0.05259162
 [937]  0.02363571  0.02688333  0.01848530 -0.00604214 -0.02716867 -0.02722847 -0.00685417  0.00519733
 [945] -0.00065930 -0.02074074 -0.05620331 -0.04628652  0.01696365 -0.01780259 -0.02860517 -0.04008761
 [953] -0.02754535 -0.01634527 -0.03466017 -0.01698841 -0.03368713  0.00137538 -0.00001726 -0.02206625
 [961] -0.02937552 -0.04308455  0.01940172  0.00874241 -0.02977777  0.00698559 -0.00778609 -0.03506433
 [969] -0.02745823 -0.04273358 -0.05083597  0.00930574 -0.00968700 -0.00903485 -0.00566552  0.00208223
 [977] -0.00790780 -0.00700300  0.04614389  0.01499214 -0.00985071 -0.00940658  0.01978817  0.00790782
 [985] -0.02914472 -0.03217463  0.00200599 -0.01098514 -0.01056695 -0.03509595 -0.02089871  0.00950951
 [993] -0.04506481 -0.01342738 -0.02697804  0.01470590 -0.01251625 -0.02685997 -0.01096756 -0.01915778
 [ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,4] #Synt
   [1] -0.0580709 -0.1255246 -0.0531943  0.0211091 -0.0245510 -0.0957503 -0.0366260 -0.0710829 -0.1038699
  [10] -0.1188227 -0.1007875 -0.0316187 -0.0501136  0.0566174 -0.0633323 -0.0683469 -0.0332834 -0.0753876
  [19]  0.0208850 -0.0601255 -0.1326091  0.0113301 -0.0607656 -0.0758129  0.0405229 -0.0387246 -0.0875871
  [28] -0.0138514 -0.0746292 -0.0794400 -0.0170989 -0.0328154 -0.0426362 -0.0259568 -0.0239526 -0.1126320
  [37] -0.0636162 -0.0660164 -0.1617342 -0.1290503 -0.1027480 -0.0177612 -0.0337322 -0.0457715 -0.1003000
  [46] -0.0518207 -0.0536642  0.0060373  0.0172356  0.0033403 -0.1160989 -0.0872466 -0.0017243 -0.1050734
  [55] -0.0278275 -0.0480012 -0.0626322 -0.0910978 -0.0555417 -0.0660440 -0.0012200 -0.0066243 -0.0041009
  [64] -0.0228487  0.0026215 -0.0468868 -0.0560579  0.0170095 -0.0285358  0.0096689 -0.0266034 -0.0441120
  [73] -0.0300320 -0.0806484 -0.0071939 -0.0654095 -0.0047029 -0.0612530 -0.0502507 -0.0747559 -0.0191391
  [82] -0.0324867 -0.0519815 -0.0173815 -0.0483023 -0.0577793 -0.0494004 -0.0273234 -0.0584371 -0.0510110
  [91] -0.0589917 -0.0362908  0.0072645  0.0116599 -0.0189496 -0.0586856 -0.0488691 -0.0263996 -0.0541288
 [100] -0.0636438  0.0251891 -0.0408183 -0.0339144 -0.0527506 -0.0193188 -0.0576638 -0.0659842 -0.0419608
 [109] -0.0619938 -0.0508279 -0.0967825 -0.0191844 -0.0533217 -0.0677140 -0.0242431 -0.0616770 -0.0086491
 [118] -0.1190887 -0.0941562 -0.0145234 -0.0840084 -0.1481985 -0.0433087 -0.0789382 -0.1013058 -0.0217750
 [127] -0.0016619 -0.1587711 -0.1009086 -0.1343771 -0.0677475 -0.0759733 -0.1116960  0.0641689 -0.1010369
 [136] -0.0623694 -0.1315117 -0.1002803 -0.0180661 -0.0216752 -0.0945420 -0.1175468 -0.0495235 -0.0533487
 [145] -0.0949900 -0.0633932 -0.0317941 -0.0084627  0.0310388 -0.0767864 -0.0680118 -0.1259753 -0.0580258
 [154] -0.0473656 -0.0623826 -0.0334204 -0.0648701 -0.0045107 -0.0505180 -0.0215068 -0.0378109 -0.0577860
 [163] -0.0263118 -0.0705493 -0.0565250 -0.0120375 -0.0847457 -0.0516550 -0.0355564 -0.0800831 -0.0192013
 [172] -0.0913282 -0.0019237 -0.0618620 -0.0763390  0.0224207  0.0373938 -0.0861595 -0.0546355 -0.0501233
 [181] -0.0650828  0.0653112 -0.0141210 -0.0884818  0.0432456  0.0112641 -0.0843061 -0.1059385  0.0035096
 [190] -0.1328496 -0.0076145 -0.0617798 -0.0695282 -0.0505623 -0.1388247 -0.1030055 -0.1089848 -0.0697045
 [199] -0.0850279 -0.0159048 -0.0294539 -0.0762410 -0.0555707 -0.0258958 -0.0671594 -0.0501643 -0.0800857
 [208] -0.0516575  0.0857000 -0.1400521 -0.0484127 -0.0762083 -0.0401478 -0.0008569  0.0131564 -0.0810089
 [217] -0.0994654 -0.0874506 -0.0201230 -0.0539670 -0.0815648 -0.1464876 -0.1169796 -0.0950423 -0.0611003
 [226] -0.0741535  0.0816886 -0.0980940  0.0192576 -0.0251506 -0.0894290  0.0014734  0.0019016 -0.0885483
 [235] -0.0079345 -0.0116365 -0.0422948 -0.0937649 -0.1224406  0.0339376  0.0549601 -0.0920948  0.0001058
 [244] -0.0414216 -0.0889287 -0.0829606 -0.0494142 -0.0312794 -0.0315852 -0.0991131 -0.1456220  0.0435692
 [253] -0.0759001 -0.0812450 -0.0643495 -0.0622179 -0.0614507 -0.0329436 -0.0117995  0.0558435 -0.0158497
 [262] -0.0913417 -0.0557749 -0.1111105 -0.0822615 -0.0159514  0.0029830 -0.0635864 -0.0359293 -0.0515109
 [271] -0.0045787 -0.0450994 -0.0616897 -0.0134001 -0.0415259 -0.0963586 -0.0584049 -0.0729831  0.0546334
 [280] -0.0345345 -0.0171604 -0.0542030 -0.0355484 -0.0987536 -0.0621494 -0.0270429 -0.0439867 -0.0855463
 [289] -0.0077347 -0.0548824 -0.0689926 -0.0244525 -0.0495526 -0.0629574 -0.0520421 -0.0500731  0.0274207
 [298]  0.0013628 -0.0103799 -0.0911320 -0.0469494 -0.0796259 -0.0732215 -0.0456949 -0.0784638 -0.1081318
 [307] -0.0290121 -0.1129437 -0.0740280 -0.0607811 -0.0585021  0.0420056 -0.0445469 -0.0316670 -0.0435510
 [316] -0.0286988  0.0064144 -0.0554086 -0.0260772 -0.0695439 -0.0337291 -0.0426746 -0.0459226 -0.0237162
 [325] -0.0643804 -0.0340856 -0.0623693 -0.0804818 -0.0266953 -0.0875567 -0.0269529 -0.0874816 -0.0704570
 [334] -0.0601661 -0.0657173 -0.0293735 -0.0516986 -0.0516427 -0.0325253 -0.1211751 -0.1092165 -0.0843056
 [343] -0.1339374 -0.0667145 -0.0438018 -0.0288992 -0.1120275 -0.0407848 -0.0485029 -0.0304792 -0.0242671
 [352] -0.1029410 -0.0309156 -0.1135623 -0.0634958 -0.0457185  0.0160404  0.0107945 -0.0239092 -0.0298240
 [361] -0.0989963  0.0211364 -0.0823153 -0.0603631 -0.1170338 -0.0884012 -0.0651281  0.0137594 -0.0370961
 [370] -0.0854204 -0.0690124 -0.0674919 -0.0152692 -0.0472213 -0.0423105 -0.0409133 -0.0037623 -0.0238612
 [379] -0.0951537 -0.0939940 -0.0640982 -0.0569727 -0.0351607 -0.0639275 -0.1161663 -0.0586784 -0.1101348
 [388] -0.0553361 -0.0865598 -0.1067731  0.0416231 -0.0664696 -0.0569529 -0.0165446 -0.0369655  0.0119604
 [397] -0.0940155 -0.0266589 -0.0041998 -0.0525600 -0.0315629 -0.0250471 -0.0145197 -0.1054418  0.0100043
 [406]  0.0010223 -0.0645040 -0.0854113  0.0294252 -0.0162946 -0.0192479 -0.0570641 -0.1231370 -0.0484145
 [415] -0.0705734 -0.0670685 -0.0880902 -0.0351656 -0.0377941 -0.0564721  0.0206583 -0.1091387 -0.0366527
 [424] -0.0672227 -0.1053186 -0.0750971 -0.0729764 -0.0547614 -0.0176312 -0.0480856  0.0134970 -0.0603522
 [433] -0.1110494 -0.0815636 -0.0460410  0.0057996 -0.1269270 -0.0375494 -0.0127186 -0.0413153 -0.0925477
 [442]  0.0056823 -0.0100834 -0.0185128 -0.0514942 -0.0949287 -0.0371059  0.0137647  0.0238620 -0.0148203
 [451] -0.0147607 -0.0327465 -0.1213411 -0.0884793 -0.0054553 -0.0484142 -0.0928860 -0.0740342 -0.0707859
 [460] -0.0962019 -0.0358688 -0.0349636 -0.0499158 -0.0167797 -0.0396238 -0.0461220 -0.0597468 -0.0330944
 [469] -0.0663076 -0.0990223 -0.1066612 -0.0292156 -0.0742786  0.0023520 -0.0587219 -0.0415469 -0.0618586
 [478] -0.0142731 -0.0314723  0.0141744 -0.0375430  0.0236752 -0.0876268 -0.0686316 -0.0097024 -0.0625698
 [487] -0.0649667  0.0005447 -0.0732771 -0.1139303 -0.1406167 -0.0135691 -0.0128949 -0.0321518 -0.0325741
 [496] -0.0682385 -0.0365471 -0.1306812 -0.0579546 -0.0492860  0.0278489 -0.0114363 -0.0536266 -0.0449961
 [505] -0.0178958 -0.0619981  0.0017643 -0.0686702 -0.0402161 -0.0635116 -0.0086282 -0.0491020 -0.0287860
 [514] -0.0009778 -0.0717235 -0.0101734  0.0311278 -0.0465415 -0.0080350 -0.0375403 -0.0984735 -0.0316285
 [523] -0.0643713 -0.1292523 -0.1463736 -0.0631842 -0.0494830 -0.0902800 -0.1149235 -0.0895128 -0.0297794
 [532] -0.0765637  0.0372551 -0.0306406 -0.0828848 -0.0136323 -0.0286491 -0.1016975 -0.0580203 -0.0027378
 [541] -0.0989857 -0.0418428 -0.0890605 -0.1246836 -0.0289774  0.0091468 -0.0028894 -0.0685231 -0.0757349
 [550] -0.0423850 -0.0342308 -0.1517858 -0.0391310  0.0083874 -0.0776377 -0.0381384 -0.0921305 -0.0753580
 [559] -0.0853246 -0.0649890 -0.0433499 -0.0283672 -0.0861330 -0.0787500 -0.0797864 -0.0419311 -0.0873742
 [568] -0.0595298 -0.0904336 -0.0338434 -0.1120734 -0.0941751 -0.0471597 -0.0347115 -0.0551496  0.0166354
 [577] -0.0444797 -0.0767432 -0.0460097 -0.0047036 -0.0377310 -0.0888965 -0.0056026 -0.0838246  0.0071702
 [586] -0.0915864 -0.0191924  0.0118730 -0.1080674  0.0145589  0.0001870 -0.0416570 -0.0264733 -0.0479717
 [595] -0.0483762 -0.1190131 -0.0117415 -0.0214343  0.0022974 -0.0759233 -0.0629609 -0.0555673  0.0058426
 [604] -0.0936200 -0.0077048 -0.0308749 -0.0507123  0.0521713 -0.0583258 -0.0118998 -0.0404513 -0.0962759
 [613] -0.0629154 -0.0771383 -0.0667545 -0.0943365 -0.0284222 -0.0934684 -0.0467909 -0.0964143 -0.0752365
 [622]  0.0028092 -0.0308024 -0.0777274 -0.0615282 -0.0728961 -0.0078966 -0.0728099 -0.0428642 -0.1239330
 [631] -0.0306936 -0.0852184 -0.0968696  0.0463589 -0.0556192 -0.0650360 -0.0753129 -0.0225595 -0.0491702
 [640]  0.0076265 -0.0575809 -0.0269407 -0.0854585 -0.0548602  0.0071432 -0.0680956 -0.0810258 -0.0262273
 [649] -0.0179745 -0.0924814  0.0092550 -0.0882873 -0.1045615 -0.0586414 -0.0372997 -0.0372705 -0.0891078
 [658] -0.0335533 -0.0757536 -0.0183747  0.0126745 -0.0574951 -0.0078025 -0.0418091  0.0039441 -0.0175751
 [667] -0.0499059  0.0133153 -0.0265012 -0.0600198 -0.0675733 -0.0824336 -0.0523336 -0.0530426 -0.0221714
 [676] -0.1632830 -0.0617365 -0.0866244 -0.0554014 -0.0530485  0.0018667 -0.0581943 -0.0529550  0.0198446
 [685] -0.0825239 -0.0410522  0.0275531 -0.0537602 -0.1160113 -0.0678019 -0.0228692 -0.0595094 -0.0199657
 [694] -0.0319110 -0.0295339 -0.1306761 -0.0030895 -0.0408108 -0.0564524 -0.0594559 -0.0170114 -0.0582647
 [703] -0.0910905 -0.1379280 -0.0118500 -0.1256541  0.0137677 -0.0317303 -0.0487279 -0.0260480 -0.0034782
 [712] -0.0243240 -0.0935811 -0.0138758 -0.0525151 -0.0512904 -0.1513964 -0.0825594 -0.0383921 -0.0849249
 [721] -0.0812503 -0.0809393 -0.0357952 -0.0639331 -0.0524798 -0.0215323  0.0074074 -0.0426281 -0.1129249
 [730] -0.0683460 -0.0330312 -0.0469217 -0.0950688 -0.1139316 -0.0834383 -0.1378749 -0.0213169 -0.1347814
 [739] -0.0772514 -0.0228314 -0.0360555 -0.0797061 -0.0251391 -0.0315608 -0.1080079 -0.1331927 -0.0721431
 [748]  0.0120728 -0.0580965 -0.0108570 -0.0542894 -0.0558066  0.0271923 -0.0840641 -0.0635376 -0.0471780
 [757]  0.0030720  0.0107691 -0.0649546 -0.0167415 -0.0375110 -0.0382323 -0.0863283 -0.0425962 -0.0473345
 [766] -0.0538690 -0.0423341 -0.0646675 -0.0137288 -0.0082135 -0.0765607 -0.0802003 -0.0485499 -0.0777823
 [775] -0.1231128 -0.0483810 -0.0640275 -0.0692970 -0.0142725 -0.0540057  0.0254805 -0.0628860 -0.1037064
 [784] -0.0331070 -0.0202918 -0.0898621 -0.0635626 -0.0144646 -0.0639838 -0.0012904 -0.0179106 -0.0208269
 [793] -0.0426377 -0.0146822 -0.0104251 -0.0336804 -0.0833945 -0.0997342 -0.1032926 -0.0899683 -0.1100991
 [802] -0.0190055 -0.0492793 -0.0420937  0.0052866 -0.0805461 -0.0617757 -0.0258974 -0.0192520 -0.0423604
 [811]  0.0410904 -0.0436443 -0.1056956 -0.0469403 -0.1172835 -0.0189209 -0.0151243 -0.0239779 -0.1144203
 [820] -0.0451806 -0.0488542 -0.0515097 -0.0721758 -0.0559085 -0.0695870 -0.0910261 -0.0806343 -0.1650156
 [829] -0.0366731 -0.0422207 -0.0147527 -0.0911082 -0.0503074 -0.1035620 -0.0524144 -0.0984727 -0.0036500
 [838] -0.0076330 -0.1016605 -0.0513939 -0.0823356 -0.0481003 -0.0990802 -0.0163779 -0.0569816 -0.1179487
 [847] -0.0492650 -0.1454959 -0.1040295 -0.1018497 -0.0051481 -0.1107469 -0.0377337 -0.0480553 -0.0152915
 [856] -0.0004498 -0.1055547 -0.0461308 -0.0001680 -0.0476518 -0.1362110 -0.0280942 -0.0418220 -0.1076138
 [865] -0.0064124  0.0363456 -0.1036619 -0.0171969 -0.0835054 -0.0893393 -0.0567807 -0.0604021 -0.1331360
 [874]  0.0099883 -0.0682999 -0.0310496 -0.0135102 -0.0746947 -0.0650920 -0.0830484 -0.0338526 -0.0573135
 [883] -0.0102278 -0.0349917 -0.0510138 -0.0163819 -0.0736709 -0.0946622 -0.1052607 -0.1272375 -0.0022206
 [892] -0.0291186 -0.0818223 -0.0257542 -0.0053992 -0.0646445 -0.0170949  0.0060151 -0.0395360 -0.0805634
 [901] -0.0748053 -0.0618500 -0.0535615 -0.0536990 -0.0398397 -0.0049859 -0.0360675 -0.0533977 -0.1031987
 [910] -0.0394403 -0.0434704 -0.0573012 -0.0514706 -0.0866545 -0.0231664 -0.0150252 -0.0396506 -0.0364783
 [919] -0.0480184 -0.1260701 -0.1235779 -0.0573749 -0.0297702 -0.0568597 -0.0452084 -0.0532208 -0.0280845
 [928] -0.0802876 -0.0633139 -0.0080881 -0.0445927 -0.0537926 -0.0233476 -0.0409836 -0.0238367 -0.0459161
 [937] -0.0742592 -0.0587913 -0.0198683 -0.0343025 -0.0732501 -0.0268379  0.0051605 -0.0485064 -0.0556865
 [946] -0.0601430 -0.0384196 -0.0653317 -0.0369426  0.0270919 -0.1234232 -0.0168269 -0.0201091 -0.0339656
 [955] -0.0782871 -0.0244635 -0.0515288 -0.0110918 -0.0381850 -0.0052963 -0.0744787 -0.0893083 -0.0543988
 [964] -0.0380151 -0.0468271 -0.0922156 -0.0428788 -0.0338061 -0.0508924 -0.0672524  0.0097903 -0.0471493
 [973] -0.0751957 -0.0475501 -0.0759731 -0.0695680  0.0248479 -0.0006723 -0.1135022 -0.1093690 -0.0366890
 [982] -0.0736169 -0.0385838 -0.1455670 -0.0756360 -0.0541503 -0.0207080 -0.0313012 -0.1082914 -0.0817355
 [991] -0.0368730 -0.0309640  0.0412238 -0.0693011 -0.0494924 -0.0848390 -0.0290980 -0.0425895 -0.0232912
[1000] -0.0520950
 [ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,5] #Lex
   [1] -0.069098 -0.116367 -0.124565 -0.176420 -0.093531 -0.116993 -0.144602 -0.122323 -0.156137 -0.085809
  [11] -0.112633 -0.055190 -0.011962 -0.140794 -0.139883 -0.083894 -0.122228 -0.095080 -0.143504 -0.095749
  [21] -0.088869 -0.074267 -0.100710 -0.078606 -0.154985 -0.140247 -0.154963 -0.139098 -0.126072 -0.111806
  [31] -0.078000 -0.112447 -0.092223 -0.150971 -0.107237 -0.066879 -0.131230 -0.019444 -0.063071 -0.045208
  [41] -0.057779 -0.104169 -0.172058 -0.122537 -0.102102 -0.078335 -0.136413 -0.163440 -0.165446 -0.075063
  [51] -0.040287 -0.116522 -0.152285 -0.086492 -0.193180 -0.156147 -0.135968 -0.154164 -0.112217 -0.111301
  [61] -0.131721 -0.141640 -0.095810 -0.149869 -0.117142 -0.060183 -0.142303 -0.179804 -0.130077 -0.183636
  [71] -0.138277 -0.125884 -0.137317 -0.046757 -0.148556 -0.139671 -0.175938 -0.124494 -0.117983 -0.077199
  [81] -0.165997 -0.175225 -0.114825 -0.131158 -0.104889 -0.094946 -0.016849 -0.130537 -0.105199 -0.158697
  [91] -0.075878 -0.138758 -0.127239 -0.081171 -0.204199 -0.099366 -0.135095 -0.113480 -0.127146 -0.130908
 [101] -0.126751 -0.140049 -0.067722 -0.082295 -0.094392 -0.094218 -0.149414 -0.124576 -0.116494 -0.126512
 [111] -0.149175 -0.102982 -0.088846 -0.127740 -0.084759 -0.112960 -0.056006 -0.094348 -0.065176 -0.147071
 [121] -0.162986 -0.088359 -0.091713 -0.129109 -0.120877 -0.151758 -0.123199 -0.137083 -0.072490 -0.058929
 [131] -0.126569 -0.065289 -0.088674 -0.152853 -0.097695 -0.098881 -0.056658 -0.110842 -0.096446 -0.156610
 [141] -0.045022 -0.044527 -0.120854 -0.135080 -0.056219 -0.120617 -0.117988 -0.156476 -0.127451 -0.095089
 [151] -0.139622 -0.048504 -0.147525 -0.152364 -0.068968 -0.123273 -0.067668 -0.170196 -0.139110 -0.143617
 [161] -0.040625 -0.076441 -0.134336 -0.102612 -0.122604 -0.144819 -0.138918  0.007022 -0.159193 -0.144185
 [171] -0.095042 -0.096850 -0.179706 -0.125446 -0.106241 -0.141298 -0.157819 -0.059944 -0.171304 -0.116033
 [181] -0.144794 -0.223248 -0.118792 -0.104402 -0.069710 -0.177247 -0.107289 -0.094903 -0.115140 -0.127881
 [191] -0.122409 -0.062454 -0.066658 -0.155928 -0.102893 -0.069690 -0.149533 -0.103472 -0.087585 -0.065465
 [201] -0.122360 -0.070461 -0.082132 -0.127566 -0.072200 -0.170826 -0.137336 -0.092408 -0.128416 -0.092785
 [211] -0.138566 -0.146117 -0.161052 -0.095728 -0.180499 -0.138197 -0.037619 -0.089301 -0.146439 -0.140211
 [221] -0.072404 -0.117175 -0.069755 -0.107917 -0.077235 -0.055443 -0.156196 -0.038115 -0.120134 -0.128018
 [231] -0.123712 -0.175368 -0.175735 -0.109667 -0.101682 -0.099033 -0.152713 -0.054651 -0.084967 -0.168308
 [241] -0.174038 -0.081800 -0.091868 -0.084585 -0.122413 -0.137865 -0.137064 -0.116848 -0.007885 -0.105092
 [251] -0.115108 -0.138097 -0.044377 -0.089559 -0.070295 -0.108877 -0.032250 -0.183496 -0.099876 -0.211644
 [261] -0.034692 -0.158677 -0.109249 -0.069533 -0.099716 -0.187962 -0.177633 -0.106010 -0.141056 -0.169463
 [271] -0.133946 -0.152625 -0.081062 -0.154919 -0.098120 -0.081864 -0.102095 -0.098583 -0.132146 -0.124000
 [281] -0.065467 -0.121637 -0.052192 -0.011481 -0.094977 -0.145679 -0.127279 -0.109214 -0.150877 -0.159692
 [291] -0.161525 -0.171950 -0.075468 -0.100357 -0.044047 -0.096893 -0.084282 -0.181009 -0.165792 -0.104579
 [301] -0.102356 -0.093110 -0.134265 -0.116732 -0.155410 -0.082044 -0.097050 -0.139107 -0.068881 -0.112775
 [311] -0.170252 -0.070816 -0.115779 -0.139770 -0.108304 -0.093576 -0.124749 -0.147805 -0.194439 -0.093760
 [321] -0.158358 -0.115964 -0.179090 -0.111356 -0.147398 -0.124595 -0.116036 -0.114652 -0.074998 -0.058483
 [331] -0.147456 -0.105224 -0.130534 -0.107753 -0.080887 -0.122855 -0.126424 -0.125247 -0.115554 -0.082291
 [341] -0.086678 -0.096080 -0.108873 -0.106874 -0.046461 -0.083879 -0.082836 -0.128702 -0.154181 -0.082319
 [351] -0.107059 -0.120464 -0.099665 -0.100041 -0.150055 -0.074635 -0.170327  0.006105 -0.108806 -0.122475
 [361] -0.055386 -0.197177 -0.092819 -0.069671 -0.108020 -0.140051 -0.113065 -0.154787 -0.100930 -0.083619
 [371] -0.057773 -0.117473 -0.132861 -0.092716 -0.128280 -0.042223 -0.155005 -0.097156 -0.099035 -0.065588
 [381] -0.069124 -0.159454 -0.120879 -0.068677 -0.157738 -0.089619 -0.052432 -0.114687 -0.124194 -0.152423
 [391] -0.146954 -0.050868 -0.152589 -0.123008 -0.115726 -0.135665 -0.098199 -0.190340 -0.111083 -0.143152
 [401] -0.132259 -0.125619 -0.133972 -0.026739 -0.138902 -0.117914 -0.110430 -0.103129 -0.130369 -0.184670
 [411] -0.113156 -0.115579 -0.101403 -0.145838 -0.041026 -0.040356 -0.150894 -0.113499 -0.081575 -0.116458
 [421] -0.186911 -0.049350 -0.169083 -0.109962 -0.078816 -0.145057 -0.084698 -0.144987 -0.111827 -0.052508
 [431] -0.159121 -0.086030 -0.132064 -0.119283 -0.212735 -0.151569 -0.067665 -0.066687 -0.147534 -0.124278
 [441] -0.066118 -0.157946 -0.184822 -0.141101 -0.148307 -0.144094 -0.158824 -0.192085 -0.110417 -0.115749
 [451] -0.140390 -0.092111 -0.082500 -0.105898 -0.104195 -0.128391 -0.124721 -0.137544 -0.103926 -0.122176
 [461] -0.015579 -0.072952 -0.140538 -0.165148 -0.185463 -0.135608 -0.097107 -0.060435 -0.021274 -0.122633
 [471] -0.106152 -0.096968 -0.039941 -0.093340 -0.143502 -0.099747 -0.119122 -0.072401 -0.176169 -0.140081
 [481] -0.144500 -0.109760 -0.112538 -0.065794 -0.078026 -0.138208 -0.062519 -0.141922 -0.130767 -0.065247
 [491] -0.118856 -0.143466 -0.182852 -0.128671 -0.175253 -0.172703 -0.093260 -0.108746 -0.086384 -0.178275
 [501] -0.145730 -0.049612 -0.152779 -0.117684 -0.183302 -0.132725 -0.131466 -0.081131 -0.118209 -0.094710
 [511] -0.209347 -0.149631 -0.161049 -0.141967 -0.085301 -0.127151 -0.144510 -0.143535 -0.184263 -0.174404
 [521] -0.096924 -0.079024 -0.113099 -0.026106 -0.147682 -0.129701 -0.119056 -0.073954 -0.105948 -0.188146
 [531] -0.096938 -0.092715 -0.145587 -0.105246 -0.089751 -0.155716 -0.169169 -0.076096 -0.107101 -0.204504
 [541] -0.019519 -0.141736 -0.101414 -0.089609 -0.134715 -0.170632 -0.124851 -0.101802 -0.132508 -0.085012
 [551] -0.187648 -0.025306 -0.125212 -0.169976 -0.084018 -0.120062 -0.137456 -0.108751 -0.149719 -0.109713
 [561] -0.145995 -0.131225 -0.068013 -0.096919 -0.102522 -0.090263 -0.127192 -0.144362 -0.102319 -0.041916
 [571] -0.079321 -0.063572 -0.101071 -0.172347 -0.169643 -0.147326 -0.151813 -0.110167 -0.074019 -0.145063
 [581] -0.121036 -0.145256 -0.168465 -0.070203 -0.170692 -0.051954 -0.080308 -0.128755 -0.132380 -0.108236
 [591] -0.123430 -0.135819 -0.061696 -0.063155 -0.111409 -0.192680 -0.136550 -0.023158 -0.048584 -0.088090
 [601] -0.083681  0.003982 -0.173000 -0.056182 -0.126635 -0.151683 -0.086074 -0.186393 -0.056276 -0.107955
 [611] -0.074466 -0.055978 -0.110506 -0.125348 -0.141874 -0.082598 -0.142706 -0.152668 -0.105907 -0.051033
 [621] -0.115839 -0.044170 -0.209931 -0.106288 -0.131804 -0.122321 -0.119186 -0.149995 -0.131558 -0.058133
 [631] -0.106718 -0.104906 -0.143172 -0.148841 -0.125667 -0.065137 -0.132165 -0.138331 -0.077691 -0.158182
 [641] -0.068526 -0.139713 -0.080221 -0.142450 -0.126029 -0.150428 -0.153236 -0.134201 -0.141065 -0.030868
 [651] -0.185758 -0.098862 -0.094789 -0.135163 -0.077312 -0.153485 -0.101439 -0.071731 -0.067044 -0.103930
 [661] -0.151210 -0.139346 -0.090040 -0.159072 -0.158595 -0.168187 -0.143937 -0.116629 -0.136383 -0.058703
 [671] -0.112681 -0.122869 -0.105477 -0.108287 -0.118301 -0.095424 -0.153006 -0.112280 -0.073689 -0.045380
 [681] -0.152159 -0.116220 -0.118116 -0.229255 -0.048394 -0.087643 -0.207431 -0.132912 -0.096813 -0.029010
 [691] -0.136544 -0.090841 -0.060851 -0.086266 -0.166369 -0.080622 -0.090513 -0.118800 -0.182897 -0.041249
 [701] -0.110380 -0.130980 -0.204663 -0.080105 -0.159053 -0.062329 -0.143764 -0.124914 -0.113763 -0.138006
 [711] -0.081738 -0.113786 -0.053474 -0.142461 -0.095527 -0.102213  0.000860 -0.065075 -0.052158 -0.128032
 [721] -0.058360 -0.075632 -0.097314 -0.070790 -0.124074 -0.089446 -0.098695 -0.105144 -0.025761 -0.126140
 [731] -0.032964 -0.105709 -0.046161 -0.091076 -0.065558 -0.087558 -0.107808 -0.030521 -0.046847 -0.081190
 [741] -0.123736 -0.111015 -0.153036 -0.125532 -0.165401 -0.061270 -0.079094 -0.163678 -0.201571 -0.113225
 [751] -0.058136 -0.146035 -0.100117 -0.146731 -0.130332 -0.147020 -0.154254 -0.170282 -0.104872 -0.141220
 [761] -0.124929 -0.143690 -0.109699 -0.114132 -0.097816 -0.142072 -0.086402 -0.115846 -0.103915 -0.094714
 [771] -0.091562 -0.079969 -0.188586 -0.048380 -0.093601 -0.061874 -0.176336 -0.087420 -0.150279 -0.095198
 [781] -0.151466 -0.072720 -0.094956 -0.097437 -0.212711 -0.121827 -0.142938 -0.113769 -0.077620 -0.163314
 [791] -0.136774 -0.019915 -0.148913 -0.112889 -0.119581 -0.089430 -0.096565 -0.066426 -0.091519 -0.117582
 [801] -0.067381 -0.160887 -0.046652 -0.132583 -0.153491 -0.131491 -0.111374 -0.126493 -0.050485 -0.089691
 [811] -0.128448 -0.071336 -0.087850 -0.171133 -0.148290 -0.120115 -0.071404 -0.117494 -0.072152 -0.130735
 [821] -0.136963 -0.110370 -0.130002 -0.088247 -0.116334 -0.094657 -0.039650 -0.026471 -0.148814 -0.127841
 [831] -0.140511 -0.129164 -0.067736 -0.042645 -0.164376 -0.051087 -0.103615 -0.139784 -0.053773 -0.097075
 [841] -0.149658 -0.011753 -0.120016 -0.099116 -0.107369 -0.159037 -0.197443 -0.105952 -0.073051 -0.117559
 [851] -0.131199 -0.103488 -0.142457 -0.068585 -0.107818 -0.094705 -0.041710 -0.124898 -0.086966 -0.090774
 [861] -0.019987 -0.146838 -0.175867 -0.061864 -0.169124 -0.202551 -0.098080 -0.027220 -0.102020 -0.125510
 [871] -0.138921 -0.163541 -0.110454 -0.176972 -0.144316 -0.069125 -0.146512 -0.036729 -0.055412 -0.028038
 [881] -0.127364 -0.134999 -0.075482 -0.124951 -0.104623 -0.157149 -0.145363 -0.130062 -0.070864 -0.074599
 [891] -0.151363 -0.123065 -0.086094 -0.106750 -0.117897 -0.130176 -0.138050 -0.111307 -0.159653 -0.034365
 [901] -0.051216 -0.135901 -0.059939 -0.126474 -0.136822 -0.131682 -0.130941 -0.117870 -0.090600 -0.137274
 [911] -0.178629 -0.018405 -0.131559 -0.146985 -0.101355 -0.061634 -0.109037 -0.044171 -0.115071 -0.077297
 [921] -0.076888 -0.094813 -0.107897 -0.076449 -0.130404 -0.045173 -0.120664 -0.075043 -0.012034 -0.142059
 [931] -0.114466 -0.031755 -0.155769 -0.103752 -0.046563 -0.078957 -0.072355 -0.082444 -0.133971 -0.142465
 [941] -0.074725 -0.126174 -0.146304 -0.147849 -0.080413 -0.118043 -0.118303 -0.093764 -0.175888 -0.138776
 [951] -0.075901 -0.138565 -0.107713 -0.080652 -0.099531 -0.102432 -0.108291 -0.099210 -0.107402 -0.090275
 [961] -0.079868 -0.125617 -0.144289 -0.130939  0.028052 -0.097770 -0.166373 -0.083253 -0.121260 -0.163023
 [971] -0.170044 -0.140098 -0.035504 -0.164303 -0.092125 -0.168608 -0.149066 -0.112349 -0.095561  0.014181
 [981] -0.143994 -0.084143 -0.135315 -0.074579 -0.144324 -0.104185 -0.180882 -0.121829 -0.116332 -0.093875
 [991] -0.140605 -0.185408 -0.151596 -0.097202 -0.091434 -0.051397 -0.115125 -0.114293 -0.149513 -0.129991
 [ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,6] #Gram_x_Synt
   [1] -0.012053919  0.014754522  0.007144885  0.003049790 -0.011411235 -0.015348895 -0.009031572
   [8]  0.009039362  0.016592231  0.020419034 -0.006198488 -0.007743556  0.007777367 -0.002665639
  [15]  0.009880354  0.010274372 -0.027354002 -0.021811222  0.032081467  0.015904213  0.021663822
  [22] -0.047943630  0.064699402 -0.003641274  0.016484393 -0.001865889 -0.001151370 -0.015335693
  [29]  0.033256514 -0.007083530  0.019777815 -0.018994960 -0.019971223  0.031737706  0.014013200
  [36]  0.029146026  0.014526868  0.023840107  0.007806686  0.008982127  0.029482104  0.029805211
  [43]  0.000862000  0.047751829  0.004780964  0.000361097  0.009224333  0.015614542  0.002960787
  [50]  0.002811210  0.000607831  0.000250492 -0.017020925  0.002097159  0.026120180  0.042016801
  [57]  0.028003300  0.074489403 -0.010505122  0.033234996  0.010848753  0.034818311 -0.006707679
  [64] -0.005343586 -0.054321958 -0.004069811 -0.046587841  0.030966741  0.006207130  0.013901873
  [71] -0.002321440  0.004574734  0.022767526  0.001576154 -0.033016234  0.014800641  0.024906337
  [78]  0.031443095 -0.021030220  0.010658900 -0.009478767  0.007670494 -0.020670162  0.067108352
  [85] -0.024463112  0.044925778 -0.021333040  0.011518457  0.039572143 -0.008955921  0.014621281
  [92]  0.015930458 -0.001467913 -0.010837968 -0.054418834  0.013063912  0.011902387  0.003953942
  [99] -0.015386521 -0.009284926  0.037822066  0.052793045  0.020167610  0.021943492  0.037433486
 [106] -0.001596520  0.042264640  0.024495051  0.009167192  0.026045424  0.012572089  0.006028263
 [113] -0.016993474  0.002136769  0.023893247  0.034608592  0.018427484 -0.006040735  0.012260518
 [120]  0.009591331  0.009626748  0.036799164  0.010020204  0.023910852  0.031850980  0.013758085
 [127]  0.005467012 -0.020167694  0.008634891  0.023729991  0.046289298 -0.006157384 -0.000566425
 [134]  0.032965204  0.031391943  0.051088323  0.019522407  0.050655783 -0.028020993 -0.018016362
 [141]  0.005923816 -0.015911805 -0.015151022  0.020693562  0.012625658  0.049636517  0.024671339
 [148] -0.010663026  0.010876388  0.006438925  0.023421056 -0.011364867  0.042585596  0.048486604
 [155]  0.031199181 -0.006564058  0.019550293  0.017265632  0.048816281 -0.054881252 -0.002269258
 [162]  0.005412218  0.029061996  0.006378123 -0.019637594  0.026610881  0.009251373  0.056563325
 [169]  0.005212768  0.030157807 -0.005499728  0.002358558  0.008711667  0.001641390  0.021277651
 [176] -0.024797339  0.021848739  0.013811629  0.015025104  0.006561826  0.046791937  0.044749528
 [183] -0.027967058  0.014661229  0.050920277  0.025678659 -0.024421968 -0.000666629  0.010964989
 [190]  0.030398099  0.010293043 -0.005542221  0.000389019 -0.011368802 -0.008085105  0.011678237
 [197] -0.005438898  0.006733277  0.021156387  0.036478931 -0.005149817  0.005196897 -0.009360939
 [204]  0.008200879  0.008228476  0.007929707  0.028210856  0.025038587  0.013782165  0.046781534
 [211]  0.050445304  0.012528888 -0.000753913 -0.035657174  0.005037405  0.050636999  0.029250586
 [218]  0.021235383  0.000724994  0.034776776 -0.027406108  0.017014528  0.013169300  0.024859776
 [225]  0.034634536  0.011830101 -0.010510100  0.017310855  0.020732427  0.018361981  0.049341755
 [232]  0.038117915  0.041547190  0.041586174  0.043954156  0.018320186  0.010922747 -0.005859799
 [239]  0.006724215  0.002060250  0.023915412  0.015623998 -0.018653858  0.015604860 -0.016343412
 [246]  0.050553638  0.014991292  0.014199034  0.044053953 -0.003562401  0.020257409  0.017183454
 [253]  0.008845914  0.045592271  0.006887554  0.003949070  0.061227832  0.006664268  0.003188033
 [260]  0.012771630  0.022009213  0.016744541 -0.000238822  0.008166556 -0.013924808  0.006805684
 [267]  0.003817505 -0.019192657  0.021892371  0.049597519 -0.001202841  0.021979227  0.004517902
 [274]  0.019087386  0.022100085 -0.011092042  0.007058215  0.037117740  0.023837507  0.008817305
 [281] -0.013990823 -0.006241586 -0.003890364 -0.034854885  0.042098293 -0.002868521 -0.013580604
 [288]  0.040512306 -0.004801704  0.004974189  0.006968378  0.000102830  0.003127398 -0.023633128
 [295]  0.033897042 -0.012044675  0.007065881 -0.003874734 -0.000754462  0.015776918  0.048706165
 [302]  0.022663981  0.020159756  0.022704555 -0.008801243 -0.023002472  0.004670624  0.023382642
 [309] -0.000852052 -0.020165830  0.011195817  0.009162465 -0.001099382 -0.021077353  0.022289281
 [316] -0.051627648  0.000157403  0.070222834 -0.006244143  0.019957914  0.030901458  0.003225820
 [323] -0.007268060 -0.003484104  0.023939353  0.038905545 -0.005255513  0.042758748  0.000372006
 [330]  0.015141637 -0.009956753  0.026870998  0.007675974  0.008967939  0.024424123  0.020832624
 [337]  0.026632741 -0.012873898 -0.014803293  0.026098420  0.039664665 -0.003050198  0.017589908
 [344]  0.007170390  0.025028954 -0.005563575  0.037158467  0.005980765  0.017965216 -0.012339523
 [351]  0.003346277  0.025558752 -0.010826337  0.015820284 -0.003262820  0.032833077  0.001429373
 [358]  0.044786861 -0.030918742 -0.002428887  0.004334986  0.006103044  0.005175016  0.030853259
 [365]  0.007642248 -0.022361483  0.019205251  0.044242301 -0.026414495 -0.010922628  0.006345946
 [372]  0.001152588  0.008882038  0.007843064 -0.002538221  0.014874287 -0.001480009 -0.017798511
 [379]  0.014858787  0.030301098  0.037023514 -0.017214374  0.039558185  0.056279300  0.007367182
 [386] -0.006101727 -0.005720232 -0.013748114 -0.010756730  0.007551587  0.006368365 -0.004272900
 [393]  0.006911902  0.010567079  0.016096658 -0.015405292 -0.005442688 -0.004323465  0.009408704
 [400]  0.047856718  0.007483488  0.007998062  0.006371931 -0.011529029  0.004587191  0.044781677
 [407]  0.023354210  0.033594966  0.044104965 -0.008467016 -0.005118294  0.033089599  0.015013548
 [414]  0.039686870 -0.009661252  0.031851627  0.019284280  0.002436709 -0.009942009  0.025920021
 [421]  0.023419027  0.006242764 -0.000823480 -0.020710263  0.016774499  0.013601631  0.069076175
 [428] -0.028404506 -0.001806817  0.019743867 -0.000571089 -0.008219648  0.008236665  0.028811928
 [435]  0.024859711  0.032736719  0.029943147  0.011351226  0.002291657  0.020581127  0.014001618
 [442]  0.014934706 -0.032167873  0.003983736  0.024181380  0.035178239  0.012927366  0.039760399
 [449] -0.005629029  0.015405629  0.012123241 -0.018592560  0.002181511  0.015171355 -0.013936037
 [456] -0.004944377  0.007431961  0.034116533  0.013772340 -0.006508610 -0.013663091 -0.012728986
 [463]  0.044357768  0.054914802 -0.013295729  0.000796607  0.055135899  0.038053120 -0.010750937
 [470] -0.003407293  0.048667024  0.039655318  0.028360000 -0.011139553 -0.018388722  0.011541109
 [477]  0.007668721  0.007457710  0.025817415  0.019856628  0.003284943 -0.015616648 -0.012828690
 [484]  0.062811535 -0.019392346  0.035210502  0.045772524  0.051318771 -0.011890894 -0.020404882
 [491]  0.005469225  0.006292418 -0.000006148  0.029995801  0.028303511 -0.014172149  0.018505936
 [498]  0.049635036 -0.002715721 -0.023563702  0.000396551  0.057023143  0.037678222  0.004514297
 [505] -0.014472306  0.031057672  0.017058218  0.041565510 -0.001717792  0.038301211  0.071781959
 [512]  0.032048461 -0.025011325  0.020458530 -0.002143665  0.039278700  0.021704579 -0.008806822
 [519]  0.018869992 -0.023280891  0.038901766  0.026530751  0.006381008  0.060991905  0.032556930
 [526] -0.031139362 -0.016647832  0.058942433  0.022156816  0.004503064  0.056212538 -0.034383235
 [533]  0.014465665  0.019392152 -0.003466331  0.014388073  0.006884957  0.009225298  0.015329753
 [540]  0.011903942  0.009005449  0.013453775 -0.013199192  0.036351493  0.045748677  0.026582619
 [547]  0.024651536 -0.037889066  0.034040895  0.008216849 -0.029054037 -0.019732689  0.012183848
 [554]  0.013898221  0.041247424  0.024024532  0.066441407  0.031490212 -0.005688212  0.003497844
 [561] -0.035014175 -0.002662352  0.014257729  0.029548115  0.016740242  0.031869100  0.004848812
 [568] -0.004331868  0.004338056 -0.018475191 -0.047282759  0.003644762  0.014951040  0.026167329
 [575]  0.043889880  0.024999386  0.002589263  0.021193174  0.016907587  0.034450189  0.018299780
 [582] -0.001110952  0.022989705 -0.016146686  0.039457983  0.063214249  0.041375807  0.003579335
 [589] -0.006704866 -0.002280280 -0.016583614  0.053267314 -0.020037257 -0.006118687  0.005912616
 [596]  0.050662193  0.028214021  0.009168763  0.018450950 -0.021918623 -0.001668109  0.036464723
 [603]  0.015674621  0.001316117 -0.015252696 -0.019400787  0.012349184  0.004159622  0.003293789
 [610]  0.026441957  0.043647602  0.030932162 -0.016300561  0.035343431  0.031774570  0.013181647
 [617]  0.005420667 -0.017318452  0.003509716  0.033422271  0.025982079  0.039929521  0.038821851
 [624] -0.018944550  0.016694112  0.006658130  0.015950420  0.009074993  0.005294001 -0.003015660
 [631]  0.015526018 -0.006717745  0.003552571 -0.018544149 -0.005691882  0.020992950 -0.012617800
 [638]  0.022345279 -0.000039778 -0.010707206  0.021541263  0.042733030  0.011347959 -0.007234615
 [645]  0.039906873 -0.013909065  0.006361666  0.020702706 -0.006860667  0.031063582  0.066136145
 [652]  0.048047799  0.032720913  0.002822924  0.023648236  0.007544186  0.018143277  0.020396678
 [659]  0.016838428  0.000498562  0.034176210  0.013587618  0.035882788  0.028280884  0.017033557
 [666]  0.020445896  0.011110946  0.000036181  0.026962838 -0.005930062  0.028811710  0.039446787
 [673]  0.029749166  0.001960003  0.022929681 -0.005712091  0.034676024 -0.017238645  0.031562640
 [680]  0.003815196  0.015994894  0.011774437  0.018343626  0.023858496  0.019682342  0.015071338
 [687]  0.048596945  0.027659239  0.065850736 -0.037221541  0.047056788  0.029936660  0.050982556
 [694]  0.023943165 -0.021755629  0.002741206  0.002990121  0.032345890  0.021159455  0.034310716
 [701]  0.040598341 -0.008942137  0.023064678  0.035793727  0.039702970 -0.002390951  0.005644896
 [708]  0.009629626 -0.033680237  0.030776276  0.001536231  0.001253315  0.004417287  0.017071821
 [715]  0.035959268  0.009330723  0.023285293 -0.004454930 -0.020623033  0.026082472 -0.022438432
 [722]  0.008329827  0.025014950  0.026363217 -0.012304137  0.049365428  0.019805067  0.052183807
 [729] -0.008430330  0.040307198 -0.018826765  0.016490785  0.061984964  0.005365587  0.044444357
 [736]  0.018585105 -0.003226290  0.021049175 -0.010551526  0.001663555  0.048180334  0.048653505
 [743]  0.015090386 -0.010431401  0.029503216  0.006924384  0.008781463  0.004555504  0.000042913
 [750]  0.017933355  0.031648791 -0.004329365 -0.045191145  0.040187157  0.072329904  0.014598781
 [757] -0.003412739  0.002453786  0.043264804  0.009777457 -0.007603551  0.027443990  0.042178667
 [764]  0.030974086  0.034262699  0.008403341 -0.036547959  0.040874872  0.035146108  0.018878237
 [771] -0.043905149  0.038710184  0.028068466  0.022390705  0.029764010  0.057009173  0.045727026
 [778]  0.012067397 -0.006341294  0.000468020  0.043068943 -0.005756593 -0.025453610  0.034412938
 [785]  0.021323645 -0.001644282  0.004806446 -0.000568906  0.010519139  0.009240913 -0.002436806
 [792] -0.021200304 -0.024830389  0.036075209 -0.014899199 -0.012150831  0.002999114  0.002242233
 [799]  0.039615899 -0.003795963 -0.034565161  0.029131107  0.028073597  0.015484004 -0.032441500
 [806] -0.009185958 -0.030228665  0.002726402 -0.017533839  0.003029689  0.028433474  0.041672016
 [813]  0.020215372 -0.003961926  0.029731908 -0.024040580 -0.003882222  0.049569214  0.036364653
 [820]  0.034613451  0.006963568  0.026591667 -0.038113123  0.027112555  0.021861104 -0.002479497
 [827]  0.021054882  0.023035508 -0.004582576 -0.025667750  0.018828927  0.019623567  0.004891520
 [834]  0.019820957  0.049042078 -0.021253791  0.032418990 -0.005225294  0.008422118  0.019205023
 [841]  0.028316707  0.027250070  0.003329716  0.002001136  0.028099533  0.034584155 -0.043788834
 [848]  0.006555839  0.047865522  0.037586149 -0.043947864  0.017350364  0.012340980  0.035062414
 [855]  0.034300392 -0.018176239  0.002475874  0.042239447 -0.018509427 -0.009978129  0.019361764
 [862] -0.014064001  0.037991739  0.014362099 -0.005386079  0.018146183  0.012368366  0.006431632
 [869]  0.063798936  0.057414183  0.010328949  0.029888270  0.030400181  0.013052428 -0.017734332
 [876]  0.006395295 -0.000151400  0.009225683  0.011822913 -0.012261209  0.003756356  0.014860968
 [883] -0.029842157  0.004751597  0.018930760  0.058817612  0.019487910  0.006237380  0.002761476
 [890]  0.024546540  0.019185811  0.026612414  0.006070896  0.030955961  0.035833762  0.006379187
 [897]  0.029716378 -0.006818472  0.009990591  0.005757794  0.025198726 -0.066378357  0.009551091
 [904]  0.004527612  0.035425435 -0.019127787  0.042022958 -0.026514777  0.003383995 -0.035409381
 [911]  0.029203736  0.015934741 -0.010481638  0.012179697  0.008202882  0.023307838 -0.035591125
 [918]  0.029511481  0.056864625  0.008800195  0.014822592 -0.023870149  0.028976823  0.022198034
 [925]  0.040705885 -0.047019665  0.033661258  0.013396326  0.028056760 -0.004169823  0.014647351
 [932]  0.014213520 -0.015714918  0.046278686 -0.007517957  0.016054498 -0.006509788  0.028289573
 [939] -0.044324242  0.024723385 -0.044921375 -0.007520176 -0.009162039  0.014690728 -0.018305795
 [946]  0.025137231 -0.001063619  0.008165955  0.023708789  0.000319906 -0.002366817  0.004398441
 [953] -0.011777940 -0.002110035 -0.000593120  0.026958964  0.005688029 -0.023181042 -0.004643152
 [960]  0.029474770  0.045304698  0.014702044  0.051003017 -0.017537408  0.029679523 -0.005188969
 [967] -0.005438543  0.026174842 -0.011049007 -0.007329470 -0.002961007  0.026495510  0.014627785
 [974] -0.014271443  0.011850878  0.044880864 -0.017744068  0.010378671  0.009961981  0.028902989
 [981]  0.015240378  0.020341042  0.032706815 -0.019006661 -0.007384688 -0.007692411  0.002395278
 [988]  0.003319761  0.001336142 -0.033420445  0.002486631 -0.019118270  0.037628086  0.030318822
 [995]  0.032768004  0.026228418 -0.007470728 -0.016561743  0.053905805 -0.043760279
 [ reached getOption("max.print") -- omitted 7000 entries ]
y_posterior$beta[,7] #Gram_x_Lex
   [1] -0.02643453 -0.01451428 -0.04366503  0.01843834 -0.00076347  0.01209294 -0.01748334 -0.02444153
   [9] -0.00861787 -0.00017053 -0.03410152 -0.01743597 -0.02001689 -0.05376000 -0.02712873 -0.06246272
  [17] -0.04161960 -0.02835483 -0.05913866 -0.03214733 -0.03096222 -0.02073950 -0.05928627 -0.03070344
  [25] -0.04068317 -0.00764850 -0.00514830 -0.02318248 -0.01631486 -0.00948557 -0.03242251 -0.00807094
  [33]  0.01616959 -0.02220997 -0.03743642 -0.06746575 -0.01641995 -0.03401093 -0.03212801 -0.00781529
  [41] -0.03783019 -0.07492733 -0.01885148 -0.04286303 -0.01136275 -0.05469012 -0.02164856 -0.01797525
  [49] -0.01709745  0.01297769  0.00492893 -0.04140623  0.00187186 -0.04469084 -0.05977066 -0.01960863
  [57] -0.02182371 -0.02860936 -0.00057314 -0.02651102 -0.03320972 -0.04223014 -0.00491344 -0.01838174
  [65] -0.00030431 -0.02783298  0.00552627 -0.02060781 -0.02448125 -0.01608656 -0.03611300 -0.05571256
  [73] -0.00834680 -0.01216059  0.02897794 -0.02601901 -0.00534867 -0.05330070 -0.05459600 -0.02066640
  [81]  0.00650506 -0.01480814  0.00188709 -0.03217758 -0.01216388 -0.04272111 -0.05887711 -0.00675317
  [89] -0.00763020  0.06333080 -0.01137966 -0.01303753 -0.02243008  0.01089048  0.00339523 -0.01322859
  [97] -0.02061289 -0.02188776 -0.03326726 -0.00747940  0.00735974 -0.03844854 -0.02681083 -0.00438416
 [105] -0.03828524 -0.01361433 -0.02964937 -0.04928472 -0.01449879 -0.03342002 -0.02643976 -0.02073341
 [113] -0.00642303 -0.03873478 -0.03927047 -0.02899113 -0.03431393 -0.00122088 -0.01698484 -0.03453561
 [121]  0.01178450 -0.02792079  0.00487644 -0.00991496 -0.02067221 -0.02640500  0.03070449 -0.00889728
 [129] -0.03004693 -0.00921610 -0.06013195 -0.01617879  0.00019325 -0.06166815 -0.01141566 -0.03581983
 [137] -0.04069232 -0.02705157 -0.03256755 -0.02324772 -0.05463163 -0.00037038  0.01894868 -0.01947937
 [145] -0.02598582 -0.06407870 -0.02393923  0.00193464 -0.01905001 -0.01622242 -0.00808539  0.00696542
 [153] -0.06268436 -0.08193457 -0.04844866 -0.03195551 -0.05555786 -0.01196152 -0.04624227  0.01525784
 [161] -0.02704867 -0.02411313 -0.00939314 -0.02052374 -0.01043394 -0.03824097 -0.01875809 -0.07668134
 [169]  0.00009467 -0.01897002 -0.01413781 -0.00341205  0.01297243 -0.04184697  0.00166877 -0.03664289
 [177] -0.02500279 -0.03159881 -0.03543930 -0.01780031 -0.00779482 -0.02567635 -0.03116231  0.01170112
 [185] -0.04849484 -0.06606359 -0.05685330 -0.04721787 -0.06716849 -0.05641866 -0.04178808 -0.04574469
 [193]  0.00157066 -0.03630107 -0.00731648 -0.02888703 -0.01615027  0.00050683  0.00577121 -0.02481859
 [201] -0.01235304 -0.06007567 -0.02038987 -0.02497667 -0.01118532 -0.02253475 -0.03849516 -0.03194632
 [209] -0.03574074 -0.01632911 -0.06890123  0.00820960 -0.01653961 -0.03018833 -0.05682885 -0.01695133
 [217] -0.04381806 -0.02098045 -0.03217932 -0.05679859  0.00067117  0.00143422 -0.02893429 -0.02479318
 [225] -0.02620596 -0.00840995 -0.00045180 -0.01668423 -0.02240569 -0.04227508 -0.07023026 -0.04341890
 [233]  0.00781580 -0.03270959 -0.02349330 -0.00357934 -0.02321941  0.01080959 -0.03787897 -0.00615312
 [241] -0.05876903 -0.03614358 -0.00551643 -0.02687662 -0.00128653 -0.03177145 -0.04168294  0.02518072
 [249] -0.09282864 -0.02642341 -0.03431145 -0.03934078  0.02870356 -0.05774151 -0.02783546 -0.03569581
 [257] -0.07292926 -0.00722331 -0.04090171 -0.01447970 -0.04589897 -0.02387058 -0.03472802 -0.01273755
 [265] -0.02319917  0.01342241 -0.04729430 -0.05245226 -0.03079458 -0.06435720  0.02829442 -0.04202410
 [273] -0.01359534 -0.01886244 -0.05440801 -0.01384375 -0.02794992 -0.04535853 -0.06550394 -0.02447065
 [281]  0.02350221 -0.01145367 -0.06212092 -0.02789046 -0.02609297 -0.01245739 -0.01901068 -0.00791684
 [289] -0.02932615 -0.04077011 -0.01799057 -0.04467382 -0.02366046 -0.01558504 -0.04466097 -0.03990633
 [297] -0.06722660 -0.00392484  0.00892352 -0.01072640 -0.04850431 -0.02594873 -0.03623498 -0.02226579
 [305] -0.01834489  0.03161110 -0.01365192  0.00087102 -0.03940325  0.01287436 -0.03159340 -0.05321306
 [313] -0.02273264 -0.03843360 -0.02206083 -0.01899047 -0.02088854 -0.04319470 -0.02249226 -0.04861357
 [321]  0.00219132 -0.04203135 -0.03798334 -0.01776994 -0.05160029 -0.05476080 -0.01920159 -0.03703027
 [329] -0.02549676 -0.04357840  0.01453175 -0.05182269 -0.00830282 -0.01437660 -0.01006008 -0.01647934
 [337] -0.03935809 -0.00697240 -0.01028355 -0.01421691 -0.04752911  0.01034952 -0.00098039 -0.01737055
 [345] -0.00807782 -0.00222755 -0.04146218 -0.02481229 -0.04292281 -0.04056561 -0.02537366 -0.04769446
 [353] -0.02568001 -0.01215094 -0.02712172 -0.00947555 -0.00800113 -0.05020939 -0.00266159  0.00703621
 [361]  0.00850367 -0.02741474 -0.00686226 -0.05661119 -0.02526093 -0.03460484 -0.01700057 -0.05144143
 [369] -0.02662308  0.01488023 -0.02894203 -0.01582288 -0.01905348 -0.02322706 -0.02525124  0.00967005
 [377]  0.01184858 -0.02068330  0.00312128 -0.00356497 -0.06371916  0.00361304 -0.03003813 -0.06700369
 [385] -0.02484764  0.00590362  0.00349995 -0.01083284 -0.01657195 -0.00540002 -0.02771871 -0.03236171
 [393] -0.03580009 -0.02339241 -0.00128578 -0.01630400  0.02760939 -0.01304896 -0.01068724 -0.04539129
 [401] -0.04127351 -0.03084810  0.00704917 -0.05166535  0.01752555 -0.03369183 -0.04827954 -0.05456975
 [409] -0.01116630 -0.04834029 -0.03261413 -0.06028317 -0.03889594 -0.02451126  0.02053563 -0.03972262
 [417] -0.04829351 -0.01445773  0.01363454 -0.01482907 -0.03484187 -0.03135330 -0.02342801 -0.00874967
 [425] -0.03670503 -0.04178076 -0.04424004 -0.00095267 -0.02989046 -0.02578494 -0.00824375 -0.01316815
 [433] -0.05947288 -0.03818290 -0.02474425  0.01076816 -0.03226907 -0.06546323 -0.03620571 -0.02867191
 [441]  0.00652991 -0.03456909  0.00060652 -0.00450392  0.00692765 -0.01575644 -0.03356154 -0.03562993
 [449]  0.02181551 -0.03599489 -0.00669726  0.02768550 -0.01518058 -0.04376911 -0.03730073 -0.01518469
 [457] -0.02897032 -0.02771215 -0.02273453 -0.00221906 -0.03668305 -0.01800505 -0.06041597 -0.06079458
 [465] -0.03540480 -0.00816749 -0.05353366 -0.00955651  0.02004832 -0.00539581 -0.04658702 -0.07028570
 [473] -0.04270199 -0.02865353  0.04171762 -0.01110867 -0.01461266 -0.04050041  0.01035467  0.00510461
 [481] -0.00284029 -0.02296470 -0.01694183 -0.04891627  0.01525564 -0.04652637 -0.05354268 -0.03472809
 [489] -0.00682341 -0.02765865 -0.00971251 -0.07216920 -0.00047392  0.00040692 -0.01326636 -0.02496738
 [497]  0.00966524 -0.02520933 -0.01923726 -0.02539457  0.01922423  0.00417531 -0.00940627 -0.02461765
 [505] -0.00314250 -0.01853697  0.00307019 -0.04976035 -0.00967822 -0.05818942 -0.07288053 -0.06726407
 [513] -0.03345950 -0.01911275 -0.00734147 -0.02455671 -0.05006015 -0.03188424 -0.04958020  0.02029221
 [521] -0.04139600 -0.03849835 -0.02962409 -0.08544189 -0.05858402 -0.00507427  0.01253951 -0.03943135
 [529] -0.02393139 -0.03988454 -0.03128781  0.02543767 -0.00332797 -0.02998702 -0.00369736 -0.01095120
 [537] -0.03690548 -0.03988520 -0.01907614 -0.03486920 -0.00848036 -0.02674762  0.00604316 -0.01899584
 [545] -0.04451674 -0.04579106 -0.05260233  0.00208135 -0.00642935 -0.02685562 -0.01388928 -0.02246385
 [553] -0.03530743 -0.05305907 -0.01627333 -0.02262527 -0.06536191 -0.01303825  0.02371155 -0.01437739
 [561]  0.01539837 -0.00257150 -0.02118073 -0.05470416 -0.02971560 -0.04779792 -0.01910332 -0.03562463
 [569]  0.01901840 -0.07113551 -0.00045447 -0.03477393 -0.04007794 -0.01873713 -0.00935164 -0.01615365
 [577] -0.01949977 -0.02209669 -0.02505321 -0.00021634 -0.02646467 -0.01916797 -0.01256380 -0.03622366
 [585] -0.00998133 -0.00967243 -0.05353197 -0.00106051  0.02830872  0.00155172 -0.01486582 -0.05610591
 [593]  0.00478220 -0.03555491 -0.00020586 -0.06580379 -0.00736039 -0.01997332 -0.04204674 -0.04269298
 [601] -0.00095659 -0.03812233 -0.03950344 -0.01183667 -0.02464647  0.02196897 -0.02767469 -0.05377603
 [609] -0.02622700 -0.02589430 -0.04512203 -0.01764576 -0.00881160 -0.03662602 -0.04531826 -0.05111312
 [617]  0.00631146  0.00300572 -0.02185265 -0.02993809 -0.04926822 -0.03895486 -0.03158369  0.00484968
 [625] -0.04499772 -0.02701169 -0.01764699 -0.04690195 -0.01877269  0.00842860 -0.03228362 -0.03044106
 [633] -0.02351278  0.00385739 -0.00772301 -0.02563380 -0.01571325 -0.05910754 -0.01366370 -0.01916929
 [641] -0.02695644 -0.01627780 -0.03852938 -0.00745844 -0.04802423 -0.00011451 -0.02839234 -0.00081342
 [649] -0.07057136 -0.02281155 -0.04421789 -0.03817229 -0.00913516  0.00622887 -0.05324860 -0.01447360
 [657] -0.01896764 -0.02803868 -0.03020571 -0.00626744 -0.00638130 -0.05052821 -0.02530809 -0.05480415
 [665] -0.03397229 -0.04305835 -0.03251531 -0.00604614 -0.00763012 -0.01283697 -0.05616155 -0.04416624
 [673] -0.02759586 -0.04491070 -0.04162526 -0.02827539 -0.04257298 -0.01517922 -0.02056468 -0.00755024
 [681] -0.04257063 -0.00056852 -0.01432991 -0.02506194  0.01542658 -0.06204788 -0.07236074 -0.02578789
 [689] -0.06081175  0.00458579 -0.04242465 -0.02385211 -0.05580995 -0.02647968 -0.01393687 -0.03061703
 [697]  0.01211544 -0.05127699 -0.02595431 -0.02536342 -0.02325093 -0.00654146 -0.01388877 -0.05333031
 [705] -0.04726698  0.00693884 -0.00918853 -0.02739936 -0.01325029 -0.04682944 -0.00957618 -0.03964492
 [713] -0.02111677 -0.08008734 -0.04465775  0.00690704 -0.03401665 -0.00908369 -0.00863984 -0.04508560
 [721] -0.00978252 -0.01768274 -0.05541814 -0.02901092  0.03097187 -0.05214321  0.00824164 -0.05784582
 [729] -0.02645794 -0.01321716 -0.01971365 -0.03832905 -0.04708200 -0.04701990 -0.05158652 -0.02120985
 [737] -0.01479490 -0.02843450 -0.02928552 -0.03602659 -0.03268052 -0.05175128 -0.02384001 -0.01491974
 [745] -0.02707868 -0.05375941 -0.01425614 -0.02529922 -0.00952068 -0.03097688 -0.01268397  0.00693706
 [753] -0.01845683 -0.06163163 -0.07336366 -0.00116291 -0.00130428 -0.03219313 -0.05806433 -0.03281992
 [761]  0.01173381 -0.04083586 -0.03575711 -0.04609556 -0.04363068 -0.06080074 -0.00624828 -0.05538195
 [769] -0.04386118 -0.04864834  0.06944225 -0.00971917 -0.02259751 -0.02683002 -0.06396183 -0.05945182
 [777] -0.04036633 -0.03509833  0.00117676  0.02096622 -0.04094549 -0.01094518  0.00407361 -0.06569276
 [785] -0.01375686 -0.01501731 -0.02862459 -0.03735221 -0.01913476 -0.02281421 -0.02460915 -0.03664815
 [793] -0.01295285 -0.04024895 -0.05770790 -0.03192626 -0.01738563 -0.00395764 -0.01674623 -0.03427450
 [801] -0.02147518 -0.05824836 -0.00375304 -0.02417545 -0.00609911 -0.00340914  0.00416113 -0.01724947
 [809] -0.01296240 -0.01114690 -0.01393601 -0.03150531 -0.07093549 -0.03727286 -0.01374972 -0.02140981
 [817] -0.01117766 -0.06389543 -0.04256110 -0.01610282 -0.05388490 -0.05037226  0.00373292 -0.03173082
 [825] -0.08562668  0.04021089 -0.02799692 -0.02697669 -0.02296435 -0.01205416 -0.01801702 -0.04131215
 [833] -0.03191431 -0.02749930 -0.03094314  0.02511232 -0.04878524  0.00203434 -0.02310371 -0.05526603
 [841] -0.04098999 -0.01760341 -0.03250831  0.01448453 -0.02583955 -0.06986302 -0.01250631  0.01021258
 [849] -0.00541269 -0.01768586  0.02768226 -0.05891068 -0.03225762 -0.03637505 -0.06597700 -0.02366252
 [857] -0.04140228 -0.01809750 -0.05058498 -0.01955154  0.00526073  0.00648315 -0.01693454  0.00147817
 [865] -0.01773661 -0.06452452 -0.05674149 -0.02629286 -0.05809171 -0.02268760 -0.04812062 -0.04948628
 [873] -0.01349378 -0.01541379 -0.02916535 -0.03984560  0.00337029 -0.01147897 -0.03951471 -0.02150401
 [881]  0.00049595 -0.02769921 -0.04851395 -0.02324474 -0.03763487 -0.05986193 -0.06646502 -0.00968342
 [889] -0.02823357 -0.00290011 -0.00151242 -0.01045226 -0.04004993 -0.03712755 -0.06166138 -0.03269784
 [897] -0.05236049  0.01094839 -0.01058729 -0.02307828 -0.03213981  0.00144466 -0.04182505 -0.00654216
 [905] -0.04707247 -0.01519781 -0.03501768  0.03061429 -0.01760438 -0.01716675 -0.00923068  0.00382860
 [913]  0.01132242  0.01623165 -0.01240650 -0.01025050 -0.01051720 -0.03890299 -0.03175194 -0.01688440
 [921] -0.02345485 -0.02446062 -0.03428995 -0.06780624 -0.03653889 -0.01374693 -0.03138218 -0.03982395
 [929] -0.00770516 -0.01537408 -0.02318678 -0.02471987 -0.00013384 -0.06435879 -0.02400762 -0.06704189
 [937] -0.00987989 -0.03854024 -0.00511353 -0.04120990 -0.02916683 -0.01186123 -0.00029060 -0.00581717
 [945] -0.00750516 -0.04196529 -0.03758306 -0.00243677 -0.00811729 -0.03648469 -0.00034186 -0.00590233
 [953]  0.01633747  0.02212211 -0.02709491 -0.05319853 -0.04668680 -0.01104105 -0.00191937 -0.04990468
 [961] -0.05102453 -0.02932010 -0.04953287  0.00019400 -0.03136559 -0.02214163 -0.01452695 -0.05092710
 [969] -0.03485967 -0.00402785 -0.01356430 -0.03125674 -0.05122640  0.03440960 -0.03092051 -0.00782390
 [977] -0.02169741 -0.01133890 -0.06916662 -0.03715976 -0.01982440 -0.01790209 -0.05805647 -0.05247393
 [985]  0.02229157  0.00520565 -0.01108463 -0.02816843 -0.00829501  0.02204537  0.00010049 -0.01426876
 [993] -0.05139977 -0.01943905 -0.03282174 -0.03749179 -0.03978463  0.01728317 -0.07666557  0.07984352
 [ reached getOption("max.print") -- omitted 7000 entries ]
# check predicts --> posterior parameter distr. back in normal space
pst_gram <- y_posterior$Gram
pst_gram
   [1] 27.10 45.49 43.30 40.76 42.06 41.16 40.56 46.59 48.83 42.73 49.29 42.67 42.40 36.39 46.27 31.79
  [17] 33.26 45.11 39.66 44.95 43.86 40.94 30.44 54.60 48.29 45.40 43.71 43.96 41.68 50.19 39.68 28.39
  [33] 34.14 35.07 40.66 38.39 42.14 22.94 36.46 19.91 25.91 38.00 38.26 45.78 42.45 23.85 48.72 33.20
  [49] 36.39 33.91 35.36 45.11 41.90 40.99 49.50 41.26 21.76 35.23 40.65 30.33 44.89 38.23 44.64 45.45
  [65] 34.25 35.27 24.77 43.69 34.73 30.68 32.31 23.00 40.21 36.37 37.36 37.25 46.40 47.31 34.55 40.30
  [81] 41.76 56.55 34.33 46.54 38.93 39.30 26.27 39.34 49.40 44.49 43.20 37.51 30.94 32.29 28.35 41.92
  [97] 47.50 39.73 22.95 39.83 31.35 45.08 39.90 38.52 29.22 43.10 51.35 34.13 38.94 49.24 31.55 34.41
 [113] 44.26 33.22 26.02 27.68 31.09 42.05 30.64 41.87 35.67 35.65 43.66 33.56 34.79 42.43 41.91 36.63
 [129] 39.21 35.79 48.94 46.32 35.95 39.94 46.28 39.84 48.18 42.32 25.70 43.10 34.56 47.16 25.78 35.01
 [145] 38.30 37.84 26.62 41.54 34.31 31.22 36.05 39.11 37.80 34.28 37.09 32.98 34.48 35.81 42.36 41.81
 [161] 40.31 43.58 50.10 42.78 36.47 43.05 31.79 32.19 37.25 35.81 37.95 28.47 31.84 34.05 44.87 32.31
 [177] 22.17 31.82 49.55 40.79 29.45 44.37 43.22 54.77 44.58 39.63 54.98 51.28 36.56 45.00 31.27 23.86
 [193] 42.13 16.51 47.97 49.88 37.96 47.25 26.66 38.17 39.13 33.27 47.04 48.38 34.10 38.01 35.12 33.49
 [209] 48.42 34.06 35.68 39.73 41.27 32.65 44.86 34.13 43.19 39.86 38.70 43.07 41.77 40.60 20.83 30.47
 [225] 26.51 43.95 37.73 52.53 45.80 31.70 45.51 31.10 34.87 35.07 49.07 48.18 40.18 48.78 54.11 36.10
 [241] 44.87 45.08 32.78 46.13 45.52 38.86 35.96 45.60 38.56 43.87 39.09 44.26 42.74 39.62 48.90 38.13
 [257] 40.59 41.14 35.83 37.83 36.02 59.41 40.59 26.56 48.87 57.62 25.46 46.69 27.00 41.20 30.75 52.91
 [273] 38.25 32.99 33.07 45.66 28.93 31.58 38.69 34.85 41.54 35.33 32.93 32.62 37.77 32.43 42.99 43.96
 [289] 41.65 33.15 29.33 44.22 22.92 46.11 36.85 49.98 28.73 27.56 28.77 40.69 35.78 41.67 40.25 32.63
 [305] 44.32 36.53 36.47 42.57 28.04 39.22 38.71 28.35 44.46 56.47 49.18 38.29 36.00 33.77 44.20 28.87
 [321] 38.33 20.25 33.74 39.28 44.92 30.41 39.61 30.46 41.88 42.55 44.29 45.76 33.44 29.04 31.13 41.64
 [337] 48.94 36.15 35.77 41.46 37.48 37.04 47.27 32.20 16.99 48.08 35.05 44.18 33.89 42.40 43.61 45.75
 [353] 41.30 35.38 36.11 31.66 39.46 47.68 37.68 61.45 42.56 41.07 40.97 44.74 32.58 30.29 45.43 45.04
 [369] 45.28 29.13 37.06 47.31 38.50 39.27 28.95 38.29 28.38 32.53 32.88 37.10 47.96 36.69 37.43 38.30
 [385] 37.65 14.95 36.84 42.16 47.18 44.37 29.24 50.52 60.91 48.74 32.40 55.94 35.24 22.54 31.98 50.68
 [401] 36.16 46.99 50.07 65.47 42.31 43.80 54.85 41.02 40.30 44.73 42.10 43.59 44.74 24.89 35.68 46.11
 [417] 38.11 33.17 47.22 31.39 42.77 35.61 50.38 38.60 43.78 28.59 41.04 43.72 35.62 41.13 35.14 38.20
 [433] 35.67 21.48 41.45 25.02 27.32 21.89 40.99 38.85 45.08 45.38 24.87 54.21 38.51 47.61 26.27 33.55
 [449] 37.81 32.45 43.44 36.16 48.37 27.53 26.82 46.29 36.37 46.15 29.24 38.18 38.68 34.07 43.56 34.92
 [465] 36.65 37.23 28.58 39.76 34.70 41.93 30.97 49.80 35.70 45.46 36.25 38.60 35.48 46.28 27.16 48.79
 [481] 44.76 35.30 49.22 24.38 40.92 24.51 38.34 17.64 38.40 31.29 32.05 49.99 28.95 47.31 36.67 40.93
 [497] 48.62 36.35 30.50 23.54 41.65 55.73 40.64 41.72 27.72 29.49 32.45 36.53 33.81 28.57 52.65 42.03
 [513] 43.94 56.48 45.29 40.57 40.73 34.75 34.62 43.79 30.52 45.03 29.95 43.24 50.97 25.91 39.40 44.74
 [529] 55.56 48.83 39.19 52.78 40.43 35.68 43.54 47.92 39.00 13.46 38.93 36.17 43.45 44.86 40.68 42.40
 [545] 33.82 36.32 43.17 50.43 44.77 32.03 51.67 61.58 47.73 38.09 41.48 35.58 31.74 34.11 45.76 50.52
 [561] 31.57 42.43 34.31 27.12 43.68 34.47 34.07 47.49 35.66 44.30 41.57 47.56 44.57 45.33 39.81 38.78
 [577] 45.16 29.17 49.81 33.69 31.10 33.70 21.47 39.75 40.98 41.29 54.30 46.65 26.27 47.22 41.44 47.06
 [593] 39.80 20.43 32.55 38.84 37.60 40.24 34.51 26.43 27.94 29.46 38.60 44.64 48.43 37.27 42.21 37.36
 [609] 34.78 32.67 46.53 24.63 40.92 23.19 42.74 42.69 55.94 45.20 42.93 37.34 21.24 48.06 38.81 36.74
 [625] 28.50 34.66 51.64 24.11 42.70 33.69 49.61 31.12 35.94 42.95 18.01 41.22 36.51 22.29 37.28 25.30
 [641] 34.58 33.80 38.33 45.29 35.00 39.46 37.98 19.67 47.55 34.04 38.66 45.94 34.64 36.24 56.24 38.48
 [657] 54.77 42.68 39.54 50.82 25.67 31.31 38.02 35.85 37.64 35.74 43.49 46.70 28.46 20.87 28.36 37.71
 [673] 49.02 42.97 38.95 35.06 38.55 42.40 46.68 45.46 42.76 38.94 33.30 47.20 27.99 36.12 38.43 11.95
 [689] 39.01 30.01 55.49 41.69 40.52 42.66 40.36 37.47 20.48 32.64 42.45 44.04 33.86 41.07 45.17 39.06
 [705] 43.55 37.45 50.01 37.62 35.11 28.81 49.75 33.99 37.93 28.95 41.33 37.26 36.87 34.66 27.85 44.85
 [721] 36.13 38.09 30.13 43.01 33.32 41.97 50.49 27.40 24.47 57.25 34.06 41.25 46.13 41.11 31.00 42.54
 [737] 46.76 46.99 38.37 42.79 38.55 49.12 30.07 47.29 48.66 43.13 45.55 50.96 39.54 39.35 38.00 31.27
 [753] 37.19 39.71 35.44 35.33 39.82 32.21 40.59 33.77 48.85 39.61 37.05 33.57 36.57 39.76 48.75 38.77
 [769] 36.73 38.05 39.74 39.14 37.44 43.66 31.98 28.20 38.18 43.10 38.99 32.14 37.49 28.45 33.25 42.86
 [785] 42.48 43.77 42.55 58.05 39.02 37.26 33.41 31.23 23.13 23.93 42.19 33.80 44.15 40.78 32.70 45.06
 [801] 30.47 50.78 46.63 32.26 46.39 42.67 39.61 37.64 49.40 37.94 38.38 47.66 44.20 46.64 40.47 21.86
 [817] 43.07 54.32 28.37 41.27 30.87 34.73 33.74 37.88 25.36 31.53 32.07 41.67 56.06 32.64 31.10 38.96
 [833] 50.25 41.98 44.30 45.42 27.49 38.47 41.56 28.39 26.27 29.15 38.43 33.03 38.22 35.87 36.67 49.87
 [849] 42.01 31.27 42.21 41.24 34.41 44.08 48.98 29.80 35.10 41.01 44.10 31.83 34.56 30.55 52.72 42.29
 [865] 44.19 38.25 42.14 37.44 46.68 41.87 46.61 25.78 46.55 46.32 31.56 31.70 45.55 33.13 25.00 45.13
 [881] 37.40 40.00 34.85 42.49 36.44 39.96 40.29 53.42 30.18 33.54 44.20 20.98 45.28 52.57 43.36 33.53
 [897] 45.77 29.51 50.31 36.12 30.79 31.57 43.87 39.79 33.53 40.33 34.81 22.62 39.85 42.30 43.57 23.37
 [913] 39.81 43.55 49.34 35.04 37.66 40.91 33.46 35.04 49.24 34.46 53.34 48.62 44.94 29.69 23.52 49.73
 [929] 23.62 36.56 42.11 46.46 45.90 32.35 30.47 42.73 49.06 50.56 20.70 53.77 28.34 28.86 32.33 41.39
 [945] 46.06 41.84 29.00 42.23 40.92 36.82 43.34 46.56 44.96 31.56 30.28 41.69 41.21 41.71 52.94 42.60
 [961] 46.08 34.73 40.12 32.32 42.53 26.66 46.65 38.65 36.72 52.23 41.33 46.19 29.88 30.67 24.31 39.51
 [977] 19.77 48.66 34.62 38.46 41.11 30.98 46.41 37.11 37.51 52.23 20.03 33.07 41.52 56.64 21.57 47.38
 [993] 33.74 52.68 27.14 18.89 44.79 54.51 38.42 42.86
 [ reached getOption("max.print") -- omitted 7000 entries ]
density_gram <- density(pst_gram)
plot(density_gram, main = "Density Plot of pst_gram", xlab = "pst_gram values", ylab = "Density", col = "red")


pst_gen <- y_posterior$Gen
pst_gen
   [1]  -0.336259   5.421016  -2.070452  -3.864327  -2.329377   9.599332   2.341612   3.888579   0.195945
  [10]  -0.988255 -10.482393   4.726151 -10.735708   4.812651   0.613476 -15.421126   7.458396  -4.367548
  [19]   3.511281   7.294645   9.841403   9.990754  -1.180291  -3.691716  -9.741715  -6.627670  -5.137832
  [28]  -0.848989 -10.407494  -6.197579 -15.935158  -5.605109  -9.054800   0.987422  -3.819461  -3.991178
  [37]  -7.966152  -5.786592  -0.008239 -10.442185  -5.798388   2.378973  -6.005132  -5.070985 -10.575037
  [46] -10.217830 -13.407330 -16.773603  -2.616412   9.115410  -4.355538 -10.593461   0.872785 -13.431707
  [55]   8.038806   8.276597  -3.279344  -5.991877  -4.478842  -9.741289  20.220092 -13.947706   0.058628
  [64]   5.310283   7.230607  -7.122810  -8.723101 -10.102857  -9.986747 -14.460826   5.285884  -5.258822
  [73] -23.001661  -0.110396  -0.230776   4.044957   4.845807   7.577506 -10.588604   2.924012  -9.978517
  [82]  -0.611442 -10.188708 -11.057308  -2.218713 -11.259309  -4.327092  -6.016482  -7.908411   6.043780
  [91]  -3.457963 -11.819914  -6.936388  -8.807306 -13.852171  -2.320043  -8.308554  -4.839289  -3.626115
 [100]  -5.470007  -9.724511   6.483118  -9.606888   5.468177   2.192224  -2.272653   1.146615  -8.998758
 [109]  -9.547409  -1.648923 -10.509915  -0.867274   4.734686  -5.956585 -10.486839  -8.223798  -5.317020
 [118]  -1.761636  -2.197854 -14.496968   1.895192  -3.609850  -0.691089  -1.329228   1.859751  -0.850321
 [127]   3.082366  -4.601353  -5.486166   3.466837   5.326725   4.752155  -5.161996   6.136557  -4.138717
 [136]   2.902578 -14.815336   3.719221  -1.080444   1.615780  -0.756766  -9.213037  -9.905852   1.042570
 [145]  -2.194072  -4.470309  -0.384314   2.931838  -7.280097  -9.708149 -16.727450   3.891873  -0.391361
 [154] -12.215520   9.142799   1.675985   0.536396  -9.714720  -7.990414  -9.127843  -3.971838   6.090579
 [163]  -6.049193   2.922225   0.985855  -3.408628  -9.722008  -2.318991  -4.309190  -1.024400   1.532153
 [172] -14.351198   7.219340   2.448024  -8.178926  -8.942536 -15.651781  -7.239871  -8.041255 -10.989651
 [181]  -5.221224  -4.209503  -9.244022   8.657323 -13.747497  -3.722136  -3.460394   3.092181  -2.266351
 [190]  11.052833   1.861565   9.756665   2.954138   0.015717  11.221877  -3.376741  -8.858695 -12.884660
 [199]  -6.217670  -1.183323   0.992734   1.752425 -10.377984   0.092506  -3.936242  19.095561   2.547117
 [208]  -3.200126 -11.509861  -8.259332   8.138252   1.107799  -0.062468   7.605979  -2.878756   1.674620
 [217]  -6.951288   1.807621   7.247213   6.302034 -14.518420  -7.739977  -7.133773  -7.593429  -6.854018
 [226]  -6.878293  -7.157974  -1.858156  -5.613795 -10.181565  -5.897846   6.099591  -8.374010 -12.892262
 [235]   6.981454  -6.266595  -5.178673 -16.330671  -9.065957 -18.787914  -6.725170 -11.151132  -5.366024
 [244]   5.917880  13.226704   4.898431   3.104295  -0.733569 -13.392922  -0.361732  -3.648410  -7.267860
 [253]  12.704164 -13.308319  -5.315701  -4.833460   0.499770 -12.044276  -4.927664  21.041837  -2.122881
 [262]   1.513560  -2.438550   2.510813  -3.732281   6.017305  -3.431176  11.495216   5.138604 -13.016560
 [271]   0.085832   0.011009  -3.057723 -17.943717  -0.865185  -2.433680   3.844409  -5.962113   0.562913
 [280]  -1.689453   6.389848   0.472891  -8.526555 -19.406529  10.686556  -3.178385   8.536322   3.091768
 [289]  -9.508803  -2.913570  -1.051159   6.647423   0.644260   2.490714   4.889970 -10.632756  -7.185918
 [298]  -8.321120   1.132598 -12.573792  -9.430872  -9.413185   2.038796  -2.418698   7.177597  -5.380919
 [307]  -4.994889  -8.003778  -8.113601  -9.660847  -6.552602  -3.885869   5.574066  -5.838091  -0.835261
 [316]   5.071017  -0.426455  -0.816985  -7.374792  -3.641848 -13.805133   3.906461  -1.748363 -11.278552
 [325]  -1.336404   2.247451  -4.762906  -9.126644  -3.003583  -2.531765 -11.627186 -10.586453 -12.351023
 [334]  -6.310643  -1.544622  -8.859113   2.006791  -6.782818  -3.922657 -10.060834 -10.288379  -5.108049
 [343]  -5.556675  -2.728294  -0.691989  -7.404462   1.461574 -15.486168  -4.040345   1.275892  -4.839467
 [352]   5.582496   1.870991 -10.945154   6.035028   6.615273   0.033980  -1.712664   5.928935  -6.709352
 [361]  -5.013635  -5.460083 -15.548563   0.643574  11.822351   0.592219  -6.085920   3.631806 -13.145286
 [370]   1.066228  -3.605192  -9.138485  12.505291 -10.529564  -3.897962 -14.133241   7.829986  -2.043486
 [379]  -1.568830  -0.140820  -7.918661 -16.303969   2.086900  -5.008017  -4.762888  -3.205484  11.430613
 [388]   3.350996  -5.672120  -2.282673   2.577225   7.769017  -6.739366  -6.693238   0.240406  -2.745810
 [397]   5.525949  -6.204752   2.753149   8.897074   0.054514  -5.123753  -5.316051  -7.852384   5.046129
 [406]  -0.349686  -0.089750  -7.706524 -10.840908  -7.127448  -0.724264  -9.376372   2.825296 -14.257384
 [415]  -4.026594   6.668552   2.101860  -9.356290  -8.883704  -7.734120   1.441323 -16.421712  -1.519138
 [424] -18.770932 -10.279811   0.309338 -15.683323  -5.870601  -0.089618  -8.222879 -11.994334  -9.402575
 [433]  -6.290123  -5.889831   9.221920   2.329413  10.664019   5.435983  -7.626526   2.330151  -3.417962
 [442]  13.386851  -2.737955  -3.633300   0.082573  -9.859663   3.344967  -3.964731 -10.367991   3.167621
 [451]  -4.510495   0.930472  -3.558075  -2.059570  -3.020554  10.767542   2.904050   5.861457  -4.422559
 [460]  -5.461730  -6.421791  -8.800073  -3.533588  -7.136701  -7.678167   4.777818  -5.220223 -15.172452
 [469]  -5.082663 -13.866035  -3.002074  -9.838850 -12.721553 -16.420930  -5.145851 -18.427130 -12.064579
 [478]  -4.937791  -4.413706   1.185228 -15.474649 -11.623900  -5.772791  -3.211692 -10.425600   0.637551
 [487]  -1.093399  -8.043331   4.159968 -12.963382  -9.302864   0.693651 -10.580614   6.308634   2.602516
 [496]   5.780803  -5.878647  -4.778002 -11.124135   0.490276   0.635072 -18.268293  -7.397223  -2.213956
 [505] -10.404636   6.548354  -0.663647   3.099890 -10.524805  -3.044989  -1.673535 -10.526193 -12.206890
 [514]  -3.959823  -3.224963 -12.303659  -4.882121  11.791215  -4.246273  -8.087787   0.311195  -1.793512
 [523]  -5.257716  -1.578111  10.526340   7.124213  -6.867733  -7.909121  -5.243312   4.636888   4.015280
 [532]  -5.189137  -9.769807  -1.886358   0.318543   1.753459  -3.440635   3.242442  -7.207645   3.891741
 [541] -10.241986  -1.300800  -2.123658  -8.561048   3.956822  10.063113 -11.863912  -1.988541   2.217851
 [550] -11.643598   0.947872   2.377501  -2.072088 -16.979405   4.237909  -6.248691  -4.908682 -11.274428
 [559] -11.109068 -10.247177 -10.807653 -12.084922  -1.860680  -7.814641   1.583978  -6.294620  -3.704085
 [568]  -2.677218  -4.712909 -11.650945   5.762367 -11.653761  -3.170230  -7.505287  -6.379574  -5.873133
 [577]  -9.605467  13.053028  11.248985  -1.943644   2.665236  -2.815999  -5.433507  -1.815993   1.496122
 [586]  14.782780  -6.448585   3.101977   2.948170   0.652649 -10.732502  -8.039938 -16.897107 -10.051772
 [595] -17.735048  -2.720669  -3.265224   1.878355   2.133142  -0.192888  -8.664137  -4.712270  -6.522238
 [604]  -7.035999   1.522853  -4.367934  -0.709598  -8.265768  -0.959071  -7.124068  -1.374159   2.677996
 [613]  -0.084338  -0.897388  -0.423820   5.044638  -2.504798  -6.926689  -9.853938  15.441779   0.559376
 [622]  -5.787111  -4.611835  -1.949169  -4.244757  -9.307143   2.895162  -7.232328  -2.817908 -18.422490
 [631]   6.954249   4.850912   2.228307  -2.762239  -2.224826  -9.025058   2.874764  -0.587615   0.677173
 [640]   0.252267  -6.381958   0.298355  -3.498125 -11.145297 -16.624314   0.223214  -6.799687  -8.600359
 [649]   4.232203  -0.369426  -0.012786   4.052832   3.373167  -2.427460  -5.169458  22.240377 -14.338425
 [658]  -7.484807  -8.888818  -2.149348   5.686771   6.222934   4.292096  -8.156881  -4.905346  -8.174250
 [667] -11.235034  -7.489959   0.673323   0.699940  -6.228764   3.349266  -6.726206  -1.518387  10.887462
 [676]   3.733590  -2.190056  11.482970  -6.392277  -7.853837  12.742159   4.848498 -14.188902  18.887564
 [685]   4.626860   2.001671  -4.638811  -2.880858  -3.037511  -1.519940 -12.281872  -0.321047   2.471579
 [694]   0.252273   7.127941  -7.363657  -2.699759   0.033462   0.211019  -0.933889  -4.583647 -11.602200
 [703]  -6.246194  12.946906  -4.279646   4.150455  -8.373680 -18.110741  -2.275151 -10.871130   2.536159
 [712]  -6.897010  -3.709795 -10.371767  -4.353305  -9.372623  -3.328026   7.562994 -18.352838  -3.725053
 [721]  -5.894845  -6.070735  -6.866515   5.597946   3.541493 -15.277935  -0.714392   4.517807 -15.756803
 [730]  -5.332781  -5.738092   7.477545   5.842889 -14.668083 -10.507044   0.921243  -5.171982 -14.365022
 [739]  -4.512105  -4.597606  -3.458893   2.230619 -15.313013   3.457920   3.877492  -2.579793 -10.236007
 [748]  -4.312396   1.315565  -2.509448  -5.823834  -3.789558   5.658204  -3.740385  -9.994625   0.289183
 [757] -18.198976  -2.117052   0.379055 -10.760162  -2.642569   8.541824  -7.981574  -6.642612 -11.236099
 [766]  -3.030748   4.340423   6.854069   6.880042  -7.588970  -1.450887  -3.323773  -2.033044 -11.896962
 [775] -12.137599  -2.185407  11.527898  -1.990899  -1.149890  -6.607528   8.949967  -5.322564   2.025328
 [784]  -5.924485 -12.547526   4.356636  -3.206642 -12.668430  -4.482629 -10.962337  -9.015587  -6.396233
 [793]   0.649867  -5.341951  -0.544155  -3.588094  -1.569619  -8.391832 -11.064584   4.296106 -11.442264
 [802] -10.375211  -1.583640   0.513151 -10.992682  -5.933961  -9.307166  -8.249038  -6.164651  -2.210997
 [811]  -2.529593   4.410264 -27.331451 -10.632110  -0.626608   3.339534   1.238447  -2.256733 -27.693039
 [820]  -1.748364   0.490824   2.245630  -2.153223   0.657139  -6.640139   1.656372  -0.351088 -11.242928
 [829]  -0.548513  -5.275882  -4.585528  -4.674580  -5.336262  -3.802008  -6.706245 -14.855536 -11.439570
 [838]  10.909850  -0.489173  -9.429571  -6.930507   4.036630 -20.486067 -21.252817  -8.012113  -1.781713
 [847] -14.819506   3.731105  -2.562823  -0.711222   4.877506  -2.908695  -4.808512  -0.304009  -3.662441
 [856]   1.504528 -11.888605 -13.701576   0.945294  -2.642560  -1.183030  -9.609107 -10.631757  -0.369818
 [865]  10.928827  -6.035046  -7.571440  -5.871314 -12.546406  -3.327989 -11.370841  -0.262228  -7.066307
 [874]  -0.782537  -9.715518  -2.098000  -0.886800   4.450389  -3.577947  -0.573379  -1.845207   2.862648
 [883]  -7.543040  -5.304020 -16.416821  -7.116444  -4.882347  -2.202597   5.437394  -0.669122 -10.578645
 [892]   3.686107 -13.922494   1.335694 -13.964651  -5.439115  -8.585105  -1.833396   7.282172  -9.805231
 [901]  -9.498753  -1.199273  -1.495989  -1.276519  14.819276 -10.985668  -1.958360  -1.087211   1.644404
 [910]  -4.154147   5.349412   1.357512 -10.704006  -3.254556  -6.822688  -4.190733  -2.175990  -2.356131
 [919]   3.305140  -6.217610  -2.479293  -1.604580   9.682701  -6.927220   1.277452   5.872328   9.717050
 [928]   1.411570   3.655255  -4.242744 -22.034471  -0.834294  -3.205084  -7.545228  -7.526234 -15.037676
 [937]   7.715554   9.053072   5.487070  -1.913270  -8.318529  -8.327044  -2.076590   1.458385  -0.208224
 [946]  -6.297460 -16.019301 -14.842226   5.339955  -5.230499  -8.618249 -11.922304  -8.313197  -4.995109
 [955] -10.273642  -5.026583 -10.264537   0.415520  -0.005511  -6.925420  -9.498108 -12.780217   5.950772
 [964]   2.694525  -9.317935   2.075808  -2.486301 -11.055316  -8.006491 -13.613982 -14.305346   2.703369
 [973]  -2.779209  -2.589081  -1.827941   0.616089  -2.297181  -2.177360  13.976722   4.708046  -2.939251
 [982]  -2.878822   6.233753   2.406556  -8.536830 -10.289464   0.601656  -3.159437  -3.099671 -11.047786
 [991]  -6.438881   3.012653 -12.948514  -4.206152  -8.424725   3.987734  -3.897745  -8.679267  -3.194952
[1000]  -6.259942
 [ reached getOption("max.print") -- omitted 7000 entries ]
density_gen <- density(pst_gen)
plot(density_gen, main = "Density Plot of pst_gen", xlab = "pst_gen values", ylab = "Density", col = "red")


pst_lex <- y_posterior$Lex
pst_lex
   [1] -28.9784 -48.8464 -48.6659 -74.4814 -39.9429 -48.3171 -57.9452 -53.0551 -67.9843 -32.7947 -48.0880
  [12] -22.1473  -4.6382 -54.1656 -54.2418 -34.8009 -49.1298 -40.7403 -58.0540 -39.4102 -35.4156 -29.7107
  [23] -43.0456 -33.3631 -59.1739 -55.3473 -63.8869 -56.3093 -52.3142 -44.8831 -31.8300 -42.8867 -39.5746
  [34] -60.0639 -42.5531 -26.0382 -53.7627  -8.1613 -24.3729 -17.3964 -23.3687 -42.9663 -61.8065 -50.8497
  [45] -40.9928 -31.5663 -57.0513 -64.9309 -68.7596 -31.3802 -16.3857 -47.7228 -62.1695 -36.2341 -82.0391
  [56] -61.0224 -55.4615 -59.5408 -45.8443 -42.4482 -54.7654 -56.8329 -40.4590 -60.8457 -46.8965 -22.3780
  [67] -51.5869 -75.8099 -52.4062 -74.0965 -52.2400 -49.0775 -58.2055 -18.7304 -58.0830 -60.0659 -70.7579
  [78] -53.3362 -48.2014 -30.7423 -70.5273 -73.0290 -45.0579 -52.5530 -39.0295 -40.3220  -6.6425 -50.4010
  [89] -42.1585 -66.1515 -31.5218 -57.1437 -52.6457 -32.3696 -77.8307 -43.2744 -56.5244 -47.7086 -50.9040
 [100] -52.5079 -56.0727 -55.3685 -26.1110 -31.4530 -37.6039 -35.7349 -61.6095 -49.8232 -45.7617 -52.9481
 [111] -65.0072 -42.4583 -34.4074 -51.7705 -32.6369 -46.1656 -22.5146 -37.4077 -27.0119 -60.8437 -68.5764
 [122] -36.8392 -34.5667 -54.1139 -47.7997 -60.6075 -46.1492 -53.2043 -29.9170 -23.4286 -50.5434 -26.3755
 [133] -35.7918 -64.6998 -39.3265 -37.5534 -22.5184 -46.8131 -38.1172 -65.2626 -18.0268 -18.3047 -48.1816
 [144] -51.7322 -22.7189 -47.9134 -46.4580 -62.2738 -51.1022 -41.1968 -57.0066 -19.3754 -60.8222 -63.8411
 [155] -27.2622 -50.8919 -28.5352 -75.3474 -55.6171 -61.7040 -17.9785 -32.2734 -55.1498 -40.7742 -49.0225
 [166] -56.4983 -54.2088   2.7606 -62.0358 -57.5506 -37.1763 -39.2469 -72.0128 -50.7302 -43.9567 -55.5483
 [177] -61.5541 -23.8100 -71.1487 -47.7458 -59.7675 -99.4391 -47.5164 -43.1114 -30.9776 -69.1521 -44.8970
 [188] -38.6891 -43.7506 -56.5714 -50.0970 -23.8125 -25.7381 -63.9534 -43.8607 -31.3135 -62.2373 -42.6637
 [199] -32.3645 -26.5889 -47.2981 -27.0559 -35.0705 -54.1000 -30.7968 -74.6078 -55.3254 -36.5379 -50.4122
 [210] -40.4320 -53.9291 -60.3227 -62.2652 -38.0795 -73.6476 -54.3904 -16.4484 -34.0917 -58.2071 -58.1659
 [221] -27.6025 -48.6308 -27.5435 -41.1779 -31.6309 -21.9580 -63.5180 -15.2432 -47.6551 -50.9665 -50.4306
 [232] -72.3619 -67.7943 -44.8980 -42.2692 -39.6455 -59.9869 -21.3759 -35.1168 -63.4726 -69.6429 -34.3614
 [243] -36.3644 -34.3067 -48.2227 -57.8380 -56.9317 -48.9925  -3.3359 -39.9114 -48.1312 -56.2780 -18.3854
 [254] -37.7972 -29.6002 -42.3780 -13.2385 -78.4219 -38.0517 -92.2381 -13.8458 -65.5922 -42.0447 -27.4635
 [265] -41.4781 -78.2570 -70.8704 -42.9243 -54.8011 -68.3926 -54.2189 -66.4858 -35.9515 -62.4033 -38.8859
 [276] -37.6137 -41.6559 -40.9528 -56.0782 -46.9772 -25.2767 -47.2714 -20.4864  -4.5802 -36.1391 -59.1391
 [287] -46.5341 -45.8537 -63.5543 -63.1922 -64.0906 -72.6739 -32.0521 -36.9963 -17.2504 -39.7410 -31.7366
 [298] -70.8326 -64.9302 -42.4879 -39.5246 -38.7591 -54.8130 -45.8022 -62.7265 -35.3236 -40.8772 -56.8053
 [309] -28.4380 -45.9468 -69.6221 -27.0037 -45.4053 -52.8309 -47.6559 -39.8191 -53.7140 -57.9904 -83.9634
 [320] -37.2964 -63.0051 -42.1295 -77.4016 -42.1952 -59.0143 -47.9888 -43.2383 -45.1163 -32.4543 -23.3728
 [331] -59.7675 -41.3630 -52.8709 -43.6518 -32.7486 -50.9338 -51.4025 -48.9997 -46.8638 -32.0118 -34.5111
 [342] -41.3383 -44.1903 -44.5899 -16.4941 -35.4117 -33.2140 -52.1811 -61.5815 -32.5321 -41.7927 -50.1412
 [353] -38.2818 -39.0862 -60.9361 -31.8912 -67.8725   2.5573 -43.4386 -50.8297 -25.2769 -78.9892 -37.6562
 [364] -28.6232 -45.4607 -53.2057 -46.9439 -60.8771 -40.1829 -32.4409 -23.2265 -45.7180 -58.4582 -36.9545
 [375] -50.2705 -16.3533 -57.5844 -40.0986 -37.2508 -26.5117 -29.0828 -61.2382 -48.2142 -28.8898 -64.6334
 [386] -40.2095 -22.2026 -47.2689 -49.3410 -61.5622 -55.9906 -20.2921 -66.0966 -47.5200 -45.6359 -55.9871
 [397] -40.6498 -79.9705 -48.7511 -56.8398 -56.8929 -48.3449 -56.3779 -11.3221 -55.5884 -47.5442 -46.1509
 [408] -40.9607 -54.0206 -69.7419 -44.0592 -45.5884 -41.5302 -59.3424 -17.5159 -16.3349 -61.3657 -44.4908
 [419] -31.3686 -47.7015 -72.5286 -19.5696 -71.5192 -45.4764 -33.1640 -60.1328 -33.8915 -53.8983 -45.9301
 [430] -20.4616 -62.0677 -33.9923 -52.4632 -51.6153 -81.0496 -58.6994 -25.5493 -27.2170 -58.1879 -48.8188
 [441] -27.4844 -66.6259 -76.3550 -61.6366 -58.4629 -59.5681 -63.7940 -78.3381 -46.3427 -43.0267 -57.8263
 [452] -35.1177 -36.2780 -43.0006 -39.9058 -53.0689 -52.4827 -57.0942 -41.7765 -49.2382  -6.5016 -28.2714
 [463] -56.2775 -65.9396 -76.2089 -52.8770 -38.3796 -23.8384  -9.2932 -51.6291 -43.7374 -37.7674 -15.8919
 [474] -40.7552 -57.4728 -40.9373 -47.3183 -27.1886 -67.2576 -59.2804 -56.3030 -43.0716 -43.5776 -26.7201
 [485] -31.0644 -55.8398 -24.3682 -53.7986 -52.9868 -25.4133 -48.9819 -56.8035 -70.4822 -54.2384 -67.5527
 [496] -63.8996 -38.6424 -42.5835 -33.7094 -66.0069 -58.6579 -20.0931 -60.2014 -47.8324 -75.5950 -52.8223
 [507] -48.3168 -33.6582 -50.5841 -36.6467 -83.6442 -60.7052 -64.2673 -59.1637 -35.4241 -53.0595 -61.0668
 [518] -55.6317 -75.1830 -70.2986 -40.1128 -29.9268 -48.7326 -10.0446 -58.1771 -52.2003 -46.9433 -29.7474
 [529] -43.0970 -81.6457 -42.3257 -38.2701 -58.6381 -38.2115 -38.7622 -63.6869 -65.7102 -31.5055 -41.3869
 [540] -79.8349  -7.7417 -56.0968 -41.0272 -41.5611 -57.7897 -72.6570 -49.1237 -39.5166 -55.7729 -33.1530
 [551] -78.9948 -10.3650 -50.6461 -66.7662 -31.3740 -49.1929 -53.4004 -43.9548 -65.1506 -42.9533 -53.6842
 [562] -56.3222 -27.8292 -38.8316 -42.6855 -37.4694 -47.9717 -60.1875 -41.0529 -16.7713 -33.0195 -24.7517
 [573] -41.7432 -75.4658 -72.3419 -61.5613 -63.9681 -40.4091 -31.3154 -56.7771 -48.8843 -56.1070 -65.9857
 [584] -27.4552 -74.0113 -21.2363 -31.9419 -51.8302 -52.5924 -45.9460 -49.8789 -53.7730 -23.9816 -25.5993
 [595] -44.8476 -76.2606 -53.0117  -9.1730 -19.2331 -33.4916 -32.8716   1.5991 -69.3964 -22.3104 -49.8355
 [606] -63.4589 -33.5658 -73.5012 -23.2802 -43.0454 -31.2704 -22.6395 -46.6249 -47.5839 -60.5269 -34.4997
 [617] -57.3566 -63.8527 -42.7551 -20.1365 -47.0733 -18.1632 -81.5471 -42.7499 -52.3330 -47.2908 -51.7209
 [628] -58.3883 -53.2710 -23.8522 -40.7875 -39.6735 -58.2886 -61.5118 -50.5434 -26.1583 -51.7632 -57.1068
 [639] -29.2980 -62.8619 -26.5933 -56.1771 -31.5166 -58.1049 -50.3175 -58.3218 -64.8691 -52.9069 -55.6022
 [650] -12.2732 -79.2374 -43.6154 -38.1620 -53.9810 -33.2270 -62.2463 -40.8734 -29.7133 -27.4666 -42.5192
 [661] -57.5553 -57.7248 -39.2309 -65.7226 -64.1557 -66.5807 -60.5698 -49.9388 -50.5718 -20.6438 -46.0346
 [672] -50.6823 -41.9241 -43.4991 -50.2306 -38.8378 -60.8491 -42.8522 -31.4606 -18.6048 -60.0479 -49.4672
 [683] -46.6660 -97.5817 -19.1757 -33.9994 -82.1649 -53.3446 -37.6726 -12.4544 -55.0835 -37.0400 -24.0872
 [694] -35.4847 -73.3366 -30.6902 -36.1618 -46.5191 -76.3363 -15.7446 -45.2504 -51.6229 -86.8014 -33.9769
 [705] -66.9848 -25.3010 -64.4380 -46.1889 -47.1887 -52.2388 -32.0120 -46.7049 -21.6026 -58.1921 -37.0749
 [716] -43.7320   0.3159 -26.5868 -21.7685 -51.9372 -22.6049 -29.9407 -36.3897 -25.7312 -50.4457 -35.6543
 [727] -40.9632 -43.5020 -10.2513 -52.8279 -12.7225 -43.1980 -19.4441 -40.7771 -27.1007 -36.8641 -43.5199
 [738] -12.4249 -18.9560 -31.3255 -52.2504 -44.4392 -62.1820 -52.0239 -71.4287 -24.2586 -33.8851 -63.2818
 [749] -82.2694 -45.3079 -23.3593 -56.8680 -38.9808 -60.5030 -54.7535 -54.5708 -60.1583 -68.4213 -43.5838
 [760] -57.2149 -52.6049 -58.4670 -45.7643 -44.0127 -38.0033 -56.6568 -33.4472 -44.9987 -43.1393 -37.3633
 [771] -35.1194 -32.1802 -73.9632 -19.5728 -38.4634 -24.0586 -71.3746 -37.4011 -61.6437 -36.0926 -61.8039
 [782] -29.5818 -40.0871 -39.2438 -86.5686 -50.1873 -57.0894 -46.1400 -33.1190 -61.9365 -54.7465  -7.7994
 [793] -59.8099 -47.0245 -47.3099 -34.7706 -38.1221 -26.3219 -37.4225 -46.6122 -27.1250 -66.2748 -20.1099
 [804] -53.9471 -66.4616 -53.8838 -44.8390 -52.1157 -20.0068 -37.2206 -49.1530 -29.7206 -36.6539 -70.9588
 [815] -66.7235 -47.8615 -28.3909 -45.7743 -27.9220 -54.7617 -54.6988 -47.5585 -54.4603 -36.2785 -42.1418
 [826] -34.9467 -16.3053 -11.0089 -58.7069 -48.9455 -56.6498 -54.7384 -28.6062 -18.1325 -66.4964 -20.5924
 [837] -38.7652 -57.5220 -20.4264 -36.1317 -57.5981  -4.4110 -50.6138 -37.8860 -45.6221 -59.0096 -74.1251
 [848] -42.7322 -28.2268 -46.4383 -54.8069 -42.4450 -57.4603 -28.3973 -43.0943 -38.0123 -17.6828 -52.1603
 [859] -35.2787 -33.8872  -8.5433 -54.4864 -71.6543 -23.4217 -70.8560 -86.8485 -41.5390 -11.5702 -43.2081
 [870] -55.2893 -54.7594 -63.8185 -46.6554 -74.0936 -56.8596 -26.1396 -60.2982 -13.4989 -20.9027 -11.8357
 [881] -50.2213 -55.3923 -30.4494 -49.7939 -40.7392 -67.2094 -57.2483 -54.2784 -27.1308 -31.5282 -62.6907
 [892] -47.1592 -34.6357 -42.4748 -48.7905 -50.8117 -59.9573 -44.7202 -69.8463 -14.3379 -20.0656 -52.2570
 [903] -22.6820 -50.8503 -55.8745 -51.2371 -52.4351 -47.4203 -36.1433 -53.5402 -68.0331  -7.3697 -55.3364
 [914] -62.7637 -40.5478 -24.6937 -43.7957 -20.0956 -46.6609 -30.5675 -31.5002 -38.3047 -48.0107 -29.9340
 [925] -51.6090 -18.0960 -48.0767 -29.3699  -4.7991 -52.5382 -44.3430 -12.4570 -68.6330 -39.7352 -17.7631
 [936] -30.1121 -31.5038 -37.0354 -53.0928 -60.2398 -30.5177 -51.5079 -59.1942 -55.4052 -33.8782 -47.8367
 [947] -44.9996 -40.1110 -73.9918 -54.4412 -30.5024 -55.0213 -43.3797 -32.8785 -39.3628 -40.4416 -44.0316
 [958] -39.9926 -45.7505 -37.7986 -34.4473 -49.7366 -59.0971 -53.8776  11.7043 -38.7647 -70.9813 -35.0141
 [969] -47.1937 -69.3786 -63.9308 -54.3442 -13.5827 -62.9035 -39.6561 -66.6570 -57.8323 -46.6185 -38.6158
 [980]   5.9377 -57.3744 -34.3530 -56.9128 -30.2741 -56.4504 -44.4584 -72.5113 -46.7703 -45.5445 -39.4245
 [991] -57.8440 -78.5168 -58.1716 -40.6264 -38.0933 -18.5861 -47.8490 -49.2881 -58.1686 -56.7038
 [ reached getOption("max.print") -- omitted 7000 entries ]
density_lex <- density(pst_lex)
plot(density_lex, main = "Density Plot of pst_lex", xlab = "pst_lex values", ylab = "Density", col = "red")


pst_synt <- y_posterior$Synt
pst_synt
   [1] -24.35131 -52.69917 -20.76281   8.89170 -10.47833 -39.53073 -14.65562 -30.80821 -45.18107 -45.43449
  [11] -43.02262 -12.68632 -19.43547  21.75472 -24.52983 -28.34679 -13.36468 -32.29432   8.43637 -24.73748
  [21] -52.88441   4.53085 -25.96000 -32.17645  15.44621 -15.26178 -36.06610  -5.59934 -30.94408 -31.87567
  [31]  -6.97469 -12.50490 -18.28703 -10.31003  -9.49701 -43.87841 -26.03715 -27.71712 -62.60267 -49.71388
  [41] -41.57826  -7.32022 -12.09175 -18.97589 -40.26837 -20.87660 -22.41765   2.39376   7.14876   1.39583
  [51] -47.26142 -35.71709  -0.70272 -44.02984 -11.78579 -18.72828 -25.52016 -35.14329 -22.67467 -25.17291
  [61]  -0.50657  -2.65407  -1.73057  -9.26134   1.04842 -17.43226 -20.29612   7.15463 -11.48298   3.89165
  [71] -10.03687 -17.17986 -12.71297 -32.31703  -2.80811 -28.09789  -1.88708 -26.21944 -20.51237 -29.76852
  [81]  -8.11528 -13.50990 -20.38195  -6.95576 -17.96194 -24.52757 -19.47918 -10.53702 -23.40539 -21.22797
  [91] -24.50250 -14.92554   3.00214   4.64755  -7.20061 -25.54565 -20.42301 -11.08873 -21.64976 -25.50318
 [101]  11.13058 -16.11606 -13.07275 -20.15522  -7.69137 -21.86173 -27.17182 -16.76477 -24.33522 -21.25146
 [111] -42.13555  -7.90353 -20.64216 -27.41937  -9.33038 -25.19000  -3.47618 -47.23524 -39.03591  -5.99887
 [121] -35.29561 -61.85269 -16.31523 -33.06003 -40.04740  -8.68174  -0.62181 -61.65123 -41.66083 -53.48208
 [131] -27.03090 -30.69510 -45.09963  27.12286 -40.67376 -23.67652 -52.32280 -42.34535  -7.13526  -9.01643
 [141] -37.87415 -48.36485 -19.72608 -20.40798 -38.40346 -25.16232 -12.50700  -3.36188  12.43114 -33.25953
 [151] -27.73813 -50.37212 -23.89048 -19.81560 -24.65750 -13.78283 -27.35446  -1.99263 -20.17228  -9.22648
 [161] -16.73277 -24.39275 -10.78803 -28.02217 -22.58134  -4.68893 -33.03993 -20.31110 -13.83125 -31.93079
 [171]  -7.50587 -37.00663  -0.76904 -24.99494 -31.57197   8.80152  14.55934 -34.23282 -22.64780 -20.60818
 [181] -26.83139  28.99298  -5.64255 -36.52907  19.21302   4.38446 -35.26799 -43.19494   1.33225 -58.77524
 [191]  -3.11285 -23.55510 -26.84706 -20.70460 -59.21539 -46.30264 -45.32562 -28.72835 -31.41872  -6.45785
 [201] -11.37352 -29.27702 -23.72231 -10.96955 -28.64547 -21.86596 -32.23255 -20.41633  33.62041 -61.07884
 [211] -18.81839 -31.42549 -15.49383  -0.34064   5.35521 -31.85330 -43.51691 -33.38458  -7.98612 -22.36017
 [221] -31.09804 -60.83112 -46.22059 -36.25814 -25.01913 -29.37346  33.17562 -39.25451   7.63121 -10.00130
 [231] -36.43561   0.60660   0.73191 -36.24088  -3.29584  -4.65505 -16.58725 -36.69077 -50.63362  12.77286
 [241]  21.94846 -38.69097   0.04184 -16.79344 -35.01369 -34.77301 -20.50019 -13.10262 -13.36356 -37.63734
 [251] -60.92641  17.73297 -31.45399 -34.28495 -27.09484 -24.20261 -25.23063 -14.04545  -4.49223  24.26257
 [261]  -6.32531 -37.71085 -21.45111 -43.90957 -34.20970  -6.62407   1.18734 -25.73293 -13.93953 -20.74891
 [271]  -1.85090 -19.61512 -27.35403  -5.38823 -16.44750 -44.28207 -23.81744 -30.30841  23.15980 -13.06967
 [281]  -6.62368 -21.04634 -13.95191 -39.42531 -23.63905 -10.96155 -16.06486 -35.90460  -3.25266 -21.68159
 [291] -27.33205 -10.31259 -21.04046 -23.19851 -20.38271 -20.52724  10.32047   0.53199  -4.05690 -37.01743
 [301] -18.11837 -33.14022 -29.86419 -17.91400 -31.62731 -46.57273 -12.21205 -46.09870 -30.56472 -24.74691
 [311] -23.87835  16.01377 -17.45532 -11.95324 -19.14936 -12.20489   2.75872 -21.70901 -11.22982 -27.65555
 [321] -13.39586 -15.49027 -19.80345  -8.97868 -25.74272 -13.11433 -23.22414 -31.65440 -11.54780 -35.00340
 [331] -10.90765 -34.37972 -28.51202 -24.35955 -26.60262 -12.16501 -20.99941 -20.18446 -13.17888 -47.16572
 [341] -43.49894 -36.26677 -54.38828 -27.82011 -15.54964 -12.19502 -44.93739 -16.51760 -19.34187 -12.04002
 [351]  -9.46549 -42.83512 -11.86695 -44.37837 -25.74988 -19.53021   6.37822   4.52173  -9.53726 -12.36464
 [361] -45.20222   8.44316 -33.39023 -24.79680 -49.26174 -33.55441 -27.02360   5.40203 -14.75932 -33.14036
 [371] -27.74808 -26.24840  -6.70973 -18.81239 -16.56273 -15.84590  -1.39521  -9.84160 -35.78880 -38.00686
 [381] -26.96687 -21.84446 -14.01041 -26.89055 -47.55926 -26.31825 -46.66904 -22.78997 -34.36908 -43.08690
 [391]  15.83545 -26.51927 -24.63361  -6.38446 -14.56413   4.92922 -38.91560 -11.17122  -1.84148 -20.84209
 [401] -13.56062  -9.62860  -6.10217 -44.68162   3.99802   0.41179 -26.94150 -33.91520  12.17826  -6.13837
 [411]  -7.48761 -22.49120 -50.44970 -19.67265 -30.13812 -27.15335 -35.78490 -13.77279 -14.52755 -23.11338
 [421]   7.99573 -43.30904 -15.47218 -27.78527 -44.33184 -31.09581 -29.19707 -20.33022  -7.23501 -18.73779
 [431]   5.25491 -23.83976 -44.09833 -35.27371 -17.48516   2.24223 -47.96688 -15.32165  -5.00824 -16.21291
 [441] -38.48260   2.39252  -4.15525  -8.07520 -20.27009 -39.20920 -14.87781   5.59844  10.00646  -5.50369
 [451]  -6.07113 -12.47786 -53.38887 -35.91870  -2.08767 -19.99054 -39.06654 -30.70088 -28.44251 -38.75415
 [461] -14.97053 -13.54555 -19.96294  -6.68634 -16.24230 -17.96254 -23.60348 -13.05144 -28.97323 -41.67291
 [471] -43.94751 -11.37182 -29.56281   1.02630 -23.48838 -17.04101 -24.55288  -5.35796 -11.98875   5.98980
 [481] -14.60721   9.28268 -33.91872 -27.87320  -3.86112 -25.25144 -25.32270   0.20617 -29.66612 -44.40420
 [491] -57.97369  -5.36438  -4.95824 -13.53727 -12.52840 -25.20109 -15.13518 -51.19278 -22.60861 -18.20870
 [501]  11.19257  -4.63097 -21.09910 -18.27261  -7.36220 -24.64910   0.64761 -28.48468 -17.19355 -24.56598
 [511]  -3.43624 -19.89127 -11.46586  -0.40689 -29.78073  -4.24026  13.13451 -18.01409  -3.27022 -15.09920
 [521] -40.75504 -11.97323 -27.71885 -49.78997 -57.66022 -25.40543 -19.49406 -36.32147 -46.75500 -38.76536
 [531] -12.99420 -31.59686  14.98321 -11.11627 -35.79368  -5.56559 -11.10526 -42.11933 -22.40724  -1.06548
 [541] -39.28853 -16.53822 -36.02337 -57.86113 -12.41474   3.88645  -1.13554 -26.58744 -31.84920 -16.52263
 [551] -14.37394 -62.27317 -15.81127   3.28754 -28.98940 -15.61139 -35.76431 -30.44422 -37.08764 -25.42892
 [561] -15.91742 -12.16047 -35.25078 -31.54451 -33.20939 -17.39785 -32.93305 -24.78752 -36.27819 -13.54057
 [571] -46.67527 -36.68015 -19.46591 -15.16711 -23.47302   6.94022 -18.71276 -28.13631 -19.46064  -1.83809
 [581] -15.22395 -34.30397  -2.18986 -32.78756   3.10229 -37.45208  -7.63015   4.77363 -42.91472   6.17501
 [591]   0.07548 -16.47234 -10.28799 -19.44233 -19.45921 -47.02399  -4.55208  -8.49014   0.90934 -28.86146
 [601] -24.72674 -22.32137   2.33850 -37.19286  -3.02854 -12.89593 -19.76881  20.52432 -24.12840  -4.74084
 [611] -16.98166 -38.95492 -26.52929 -29.26171 -28.44603 -39.40880 -11.40694 -39.05061 -18.87705 -38.06207
 [621] -30.55623   1.15500 -11.92701 -31.25059 -24.40530 -28.16242  -3.42314 -28.30652 -17.33691 -50.89484
 [631] -11.72195 -32.21901 -39.40549  19.13042 -22.34903 -26.11761 -29.47105  -9.30030 -18.53750   3.02518
 [641] -22.34358 -10.81746 -33.57657 -22.34866   2.84860 -26.36589 -34.25748 -10.32650  -7.07460 -36.79128
 [651]   3.93782 -38.94444 -42.10226 -23.39433 -16.02512 -15.09043 -35.89840 -13.89460 -31.03753  -7.51150
 [661]   4.81620 -23.78919  -3.39755 -17.24389   1.59250  -6.94309 -20.97250   5.69577  -9.81385 -21.10706
 [671] -27.58972 -33.98208 -20.78815 -21.29318  -9.40457 -66.54288 -24.51641 -33.04812 -23.64883 -21.75009
 [681]   0.73540 -24.75086 -20.90443   8.41424 -32.71025 -15.91832  10.87985 -21.55321 -45.15703 -29.11600
 [691]  -9.21337 -24.25629  -7.90129 -13.12002 -12.99296 -49.78293  -1.23356 -15.96571 -23.50895 -22.69716
 [701]  -6.96772 -22.94041 -38.53718 -58.55726  -4.98132 -51.05118   6.16163 -11.72005 -20.19649  -9.84647
 [711]  -1.36153  -9.97492 -37.82198  -5.65952 -20.37201 -21.93195 -55.70008 -33.73665 -16.02173 -34.42705
 [721] -31.47826 -32.04373 -13.37714 -23.23739 -21.31706  -8.57823   3.07223 -17.62472 -44.97780 -28.59966
 [731] -12.74834 -19.16190 -40.06595 -51.02756 -34.49882 -58.09728  -8.59807 -54.93950 -31.26724  -8.80499
 [741] -15.20951 -31.89230 -10.19734 -13.06538 -46.58920 -52.78970 -30.90480   4.65841 -23.64628  -4.34041
 [751] -21.81306 -21.70252  10.58018 -34.62581 -26.66712 -17.48641   1.19595   4.31790 -26.98086  -6.77289
 [761] -15.77848 -15.53447 -36.00233 -16.41273 -18.38031 -21.45494 -16.38114 -25.10203  -5.69489  -3.23795
 [771] -29.35990 -32.27348 -18.99448 -31.47673 -50.61456 -18.81009 -25.86430 -29.64113  -5.84481 -20.46607
 [781]  10.37991 -25.57893 -43.78698 -13.32596  -8.23096 -37.00055 -25.35604  -5.86072 -27.29687  -0.48841
 [791]  -7.15930  -8.15663 -17.09932  -6.11027  -4.12015 -13.08836 -32.91704 -39.53651 -42.24379 -35.65031
 [801] -44.34649  -7.81423 -21.24295 -17.10757   2.28514 -32.98070 -24.85497 -10.65769  -7.62813 -17.57094
 [811]  15.70680 -18.17921 -44.11096 -19.42440 -52.73989  -7.53145  -6.01140  -9.33237 -44.30548 -18.90400
 [821] -19.48719 -22.17985 -30.20951 -22.97606 -25.19150 -33.60446 -33.17107 -68.76233 -14.44526 -16.14729
 [831]  -5.93924 -38.58683 -21.24243 -44.06328 -21.16556 -39.71380  -1.36448  -3.13649 -38.63809 -19.11934
 [841] -31.65152 -18.05493 -41.77041  -6.25583 -24.19724 -43.72714 -18.44537 -58.72406 -40.21309 -40.22242
 [851]  -2.14782 -45.42743 -15.19872 -19.89354  -6.10678  -0.18041 -44.78073 -19.24607  -0.06810 -17.78125
 [861] -58.30230 -10.40872 -17.00294 -40.76609  -2.68085  15.53831 -43.90669  -7.30956 -35.35776 -39.33276
 [871] -22.35506 -23.53042 -56.25923   4.17221 -26.87756 -11.73811  -5.55146 -27.46072 -24.55612 -35.07348
 [881] -13.33364 -23.49070  -4.12418 -13.92962 -19.85207  -6.99353 -28.98018 -39.48200 -40.31791 -53.81739
 [891]  -0.91816 -11.14657 -32.91532 -10.23921  -2.23213 -25.20885  -7.41430   2.41450 -17.26597 -33.62661
 [901] -29.31389 -23.75694 -20.26744 -21.56927 -16.24884  -1.93749 -14.42621 -21.46492 -41.17690 -15.36296
 [911] -16.51955 -22.94950 -21.62614 -36.96359  -9.26122  -6.01828 -15.91384 -16.59503 -19.45556 -49.89155
 [921] -50.66363 -23.16990 -13.23625 -22.25945 -17.87204 -21.32124 -11.17846 -31.42424 -25.25672  -2.98681
 [931] -17.26051 -21.10476 -10.26911 -15.68548  -9.09234 -17.50595 -32.33365 -26.40359  -7.86356 -14.48394
 [941] -29.91467 -10.94372   2.08461 -18.15117 -23.45498 -24.35419 -14.60036 -27.93869 -15.50684  10.61345
 [951] -49.63467  -6.67226  -8.09188 -13.84087 -30.95266  -9.65144 -20.93771  -4.46800 -16.25365  -2.21627
 [961] -32.12069 -35.34032 -22.25100 -15.62396 -19.53970 -36.55948 -18.25886 -14.21196 -19.78934 -28.57425
 [971]   3.67296 -18.26578 -28.77689 -18.17131 -32.69687 -27.45477   9.62470  -0.27869 -45.87856 -45.83430
 [981] -14.59774 -30.05189 -16.20796 -59.15906 -29.55102 -23.09375  -8.28151 -12.00420 -42.39101 -34.32105
 [991] -15.14867 -13.08025  15.79383 -28.95502 -20.61047 -30.68996 -12.08274 -18.35118  -9.04691 -22.70079
 [ reached getOption("max.print") -- omitted 7000 entries ]
density_synt <- density(pst_synt)
plot(density_synt, main = "Density Plot of pst_synt", xlab = "pst_synt values", ylab = "Density", col = "red")


pst_gramxlex <- y_posterior$Gram_x_Lex
pst_gramxlex
   [1] -11.08280  -6.08654 -17.04219   7.76664  -0.32583   4.98926  -6.99529 -10.58977  -3.74560  -0.06514
  [11] -14.54703  -6.99544  -7.76189 -20.65629 -10.50493 -25.90487 -16.71278 -12.14217 -23.89411 -13.22386
  [21] -12.33252  -8.29381 -25.32766 -13.02649 -15.50731  -3.01404  -2.11874  -9.37160  -6.76210  -3.80437
  [31] -13.22596  -3.07535   6.93447  -8.82166 -14.84416 -26.26701  -6.71855 -14.27618 -12.41271  -3.00698
  [41] -15.29811 -30.89310  -6.75717 -17.76977  -4.55855 -22.03309  -9.04184  -7.12720  -7.09147   5.42318
  [51]   2.00446 -16.94352   0.76287 -18.71466 -25.31994  -7.64947  -8.89003 -11.03067  -0.23393 -10.10203
  [61] -13.79092 -16.92184  -2.07346  -7.45063  -0.12170 -10.34707   2.00036  -8.66828  -9.85124  -6.47480
  [71] -13.62523 -21.69970  -3.53309  -4.87064  11.31213 -11.17397  -2.14618 -22.81392 -22.28687  -8.22641
  [81]   2.75818  -6.15772   0.73978 -12.87762  -4.52259 -18.13325 -23.21772  -2.60416  -3.05531  26.35757
  [91]  -4.72542  -5.36157  -9.26975   4.34086   1.29011  -5.75697  -8.61314  -9.19346 -13.30402  -2.99624
 [101]   3.25198 -15.18022 -10.33424  -1.67478 -15.24370  -5.16032 -12.20625 -19.69192  -5.68986 -13.97159
 [111] -11.50351  -8.54170  -2.48600 -15.68126 -15.11498 -11.83791 -13.79225  -0.48374  -7.03722 -14.26591
 [121]   4.94865 -11.63486   1.83680  -4.15059  -8.16602 -10.52790  11.48945  -3.44841 -12.39657  -3.66313
 [131] -23.99062  -6.53397   0.07796 -26.06522  -4.59210 -13.59520 -16.17098 -11.41517 -12.86337  -9.67061
 [141] -21.87614  -0.15224   7.54642  -7.45027 -10.49930 -25.43459  -9.41680   0.76855  -7.62924  -7.02371
 [151]  -3.29645   2.78191 -25.80960 -34.28899 -19.14774 -13.17859 -23.42572  -5.28418 -18.46439   6.54554
 [161] -11.96944 -10.17661  -3.85109  -8.14928  -4.16733 -14.89737  -7.30955 -30.15883   0.03682  -7.56035
 [171]  -5.52645  -1.38173   5.18603 -16.90540   0.68987 -14.38552  -9.73432 -12.54884 -14.68861  -7.31741
 [181]  -3.21254 -11.39522 -12.45271   4.82797 -21.54590 -25.72283 -23.77677 -19.23961 -25.50597 -24.93399
 [191] -17.08541 -17.43908   0.60626 -14.86346  -3.11639 -12.97578  -6.71095   0.20881   2.13139 -10.07740
 [201]  -4.76982 -23.06568  -8.70241 -10.58015  -4.76932  -9.82113 -15.48771 -12.62444 -14.01491  -7.11118
 [211] -26.78723   3.38389  -6.38233 -12.00129 -23.13706  -6.66229 -19.15944  -8.00509 -12.77143 -23.53392
 [221]   0.25577   0.59464 -11.42155  -9.45258 -10.72829  -3.32999  -0.18340  -6.67195  -8.87881 -16.81241
 [231] -28.60706 -17.87764   3.00827 -13.38061  -9.75908  -1.43186  -9.10538   4.22714 -15.64861  -2.31562
 [241] -23.47033 -15.17662  -2.18224 -10.89571  -0.50625 -13.31124 -17.29188  10.54767 -39.29760 -10.02728
 [251] -14.33420 -16.01155  11.89078 -24.36071 -11.71741 -13.88292 -29.94694  -3.07941 -15.57355  -6.28970
 [261] -18.31991  -9.84941 -13.35457  -5.02919  -9.64328   5.57382 -18.82820 -21.22498 -11.94711 -25.92636
 [271]  11.43854 -18.27723  -6.02673  -7.58474 -21.55182  -6.35768 -11.39573 -18.83192 -27.77062  -9.26056
 [281]   9.07170  -4.44639 -24.38567 -11.12728  -9.92234  -5.04926  -6.94229  -3.32099 -12.33312 -16.10484
 [291]  -7.12478 -18.84269 -10.04503  -5.74118 -17.49090 -16.35831 -25.30949  -1.53215   3.48769  -4.35437
 [301] -18.71864 -10.79530 -14.77440  -8.72793  -7.39130  13.60423  -5.74622   0.35518 -16.26410   5.24040
 [311] -12.89293 -20.28800  -8.90661 -14.50790  -9.69912  -8.07590  -8.98401 -16.92210  -9.68588 -19.32864
 [321]   0.87023 -15.25671 -16.37896  -6.72738 -20.63030 -21.07194  -7.14814 -14.55891 -11.02929 -17.41432
 [331]   5.88068 -20.35850  -3.35870  -5.81921  -4.07108  -6.82461 -15.98549  -2.72462  -4.16649  -5.52780
 [341] -18.91642   4.44987  -0.39758  -7.24134  -2.86723  -0.93993 -16.61832 -10.04806 -17.11601 -16.02527
 [351]  -9.89715 -19.83407  -9.85706  -4.74392 -10.99618  -4.04720  -3.18148 -21.03600  -1.06165   2.91694
 [361]   3.88003 -10.95138  -2.78221 -23.25480 -10.62253 -13.12851  -7.05198 -20.19985 -10.59192   5.77002
 [371] -11.63345  -6.15174  -8.37273  -9.25222  -9.88393   3.74481   4.39399  -8.53077   1.17318  -1.44057
 [381] -26.80731   1.38498 -11.96895 -28.18535 -10.16310   2.64721   1.48176  -4.46050  -6.57647  -2.17727
 [391] -10.54479 -12.90808 -15.48221  -9.02714  -0.50654  -6.71943  11.42145  -5.46784  -4.68606 -17.99849
 [401] -17.73361 -11.85891   2.96250 -21.87981   7.00383 -13.57211 -20.16226 -21.66169  -4.62116 -18.21317
 [411] -12.68783 -23.76064 -15.91971  -9.95856   8.76671 -16.07861 -19.61040  -5.66202   5.24045  -6.06803
 [421] -13.48623 -12.43173  -9.88906  -3.61533 -15.43913 -17.29537 -17.69554  -0.35360 -12.26617 -10.04655
 [431]  -3.20959  -5.20022 -23.60164 -16.50657  -9.39616   4.16322 -12.18118 -26.71733 -14.25799 -11.25066
 [441]   2.71351 -14.55656   0.24994  -1.96454   2.72647  -6.50379 -13.45644 -14.49271   9.14819 -13.36813
 [451]  -2.75456  10.54917  -6.67214 -17.76054 -14.27582  -6.26888 -12.17748 -11.48780  -9.13193  -0.89332
 [461] -15.31045  -6.97502 -24.16436 -24.23144 -14.51255  -3.18041 -21.14783  -3.76852   8.75757  -2.26915
 [471] -19.18211 -27.36611 -16.99069 -12.50369  16.68466  -4.55582  -5.79849 -15.20503   3.94415   2.15708
 [481]  -1.10498  -9.00407  -6.55428 -19.86286   6.07111 -18.77434 -20.86775 -13.14598  -2.76136 -10.77018
 [491]  -3.99846 -28.54187  -0.18223   0.17132  -5.10208  -9.21793   4.00227  -9.86344  -7.50296  -9.38078
 [501]   7.72605   1.69071  -3.70008  -9.99601  -1.29277  -7.36798   1.12692 -20.63738  -4.13725 -22.50630
 [511] -29.03635 -27.25299 -13.32766  -7.95343  -3.04714 -10.23562 -21.12549 -12.33987 -20.18263   8.16119
 [521] -17.12236 -14.57439 -12.75331 -32.89061 -23.04698  -2.03969   4.93915 -15.85628  -9.72704 -17.26462
 [531] -13.65250  10.49375  -1.33830 -10.87912  -1.59589  -4.47098 -14.30623 -16.50827  -7.36550 -13.57149
 [541]  -3.36353 -10.57110   2.44292  -8.80537 -19.07385 -19.45939 -20.67716   0.80730  -2.70263 -10.46810
 [551]  -5.83186  -9.20088 -14.26603 -20.80125  -6.07376  -9.26067 -25.36502  -5.26524  10.30146  -5.62392
 [561]   5.65336  -1.10229  -8.66395 -21.90737 -12.36346 -19.83287  -7.19654 -14.83119   7.62498 -28.46925
 [571]  -0.18910 -13.53638 -16.54204  -8.18663  -3.97941  -6.73923  -8.20265  -8.09806 -10.59554  -0.08454
 [581] -10.67756  -7.39254  -4.91080 -14.16270  -4.31856  -3.95288 -21.28618  -0.42638  11.23264   0.65814
 [591]  -6.00070 -22.18816   1.85836 -14.40884  -0.08279 -25.98121  -2.85354  -7.91139 -16.64449 -16.22455
 [601]  -0.37557 -15.31186 -15.81296  -4.69941  -9.68820   9.17577 -10.78678 -21.15587 -10.84749 -10.31658
 [611] -18.94301  -7.13505  -3.71448 -13.88901 -19.30800 -21.34242   2.53289   1.25496  -8.81499 -11.81149
 [621] -20.00478 -16.01828 -12.22957   1.94896 -17.84611 -10.43206  -7.65006 -18.23001  -7.59199   3.45741
 [631] -12.32927 -11.50362  -9.55849   1.59153  -3.10258 -10.29148  -6.14636 -24.37288  -5.15046  -7.60405
 [641] -10.45811  -6.53578 -15.13164  -3.03771 -19.15458  -0.04432 -11.99908  -0.32025 -27.78575  -9.06957
 [651] -18.81634 -16.83028  -3.67537   2.48431 -22.87968  -5.85969  -7.63711 -11.61068 -12.37136  -2.56205
 [661]  -2.42483 -20.90540 -11.02077 -22.60569 -13.71824 -17.01228 -13.66280  -2.58627  -2.82543  -4.51321
 [671] -22.92800 -18.20036 -10.96012 -18.02768 -17.65806 -11.50105 -16.90380  -5.78791  -8.77659  -3.09499
 [681] -16.77349  -0.24174  -5.65577 -10.62662   6.11170 -24.06344 -28.58249 -10.33702 -23.65367   1.96860
 [691] -17.09330  -9.72008 -22.09085 -10.88672  -6.13099 -11.65006   4.83747 -20.06165 -10.80636  -9.68036
 [701]  -9.52355  -2.57491  -5.87232 -22.61424 -19.87235   2.81585  -4.11223 -10.12017  -5.49102 -17.70410
 [711]  -3.74859 -16.25900  -8.52936 -32.68030 -17.32294   2.95291 -12.49487  -3.71006  -3.60520 -18.26989
 [721]  -3.78815  -6.99734 -20.71325 -10.54190  12.57897 -20.77675   3.41824 -23.91922 -10.52875  -5.52893
 [731]  -7.60805 -15.65198 -19.83230 -21.04243 -21.32242  -8.92506  -5.96735 -11.57556 -11.84872 -13.89458
 [741] -13.78559 -20.70129  -9.67035  -6.17604 -11.67093 -21.28363  -6.10481  -9.76233  -3.87414 -12.38473
 [751]  -5.09527   2.69713  -7.18110 -25.37977 -30.79423  -0.43096  -0.50777 -12.90881 -24.11727 -13.27833
 [761]   4.93520 -16.59259 -14.90532 -17.76146 -16.94168 -24.21714  -2.41745 -21.49588 -18.19651 -19.18167
 [771]  26.62805  -3.90927  -8.83975 -10.85321 -26.27467 -23.11637 -16.30323 -15.00900   0.48189   7.94392
 [781] -16.68108  -4.45070   1.71859 -26.44843  -5.58010  -6.17975 -11.41604 -15.13558  -8.16104  -8.63550
 [791]  -9.83710 -14.35381  -5.19395 -16.75213 -22.81257 -12.40660  -6.85898  -1.56773  -6.84348 -13.57445
 [801]  -8.64246 -23.95451  -1.61754  -9.82443  -2.63632  -1.39525   1.67373  -7.09858  -5.13594  -4.62313
 [811]  -5.32645 -13.12205 -29.59073 -15.42300  -6.17675  -8.52224  -4.44272 -24.87501 -16.46662  -6.73669
 [821] -21.49467 -21.68990   1.56183 -13.03799 -31.00383  14.83748 -11.51240 -11.21921  -9.04493  -4.60955
 [831]  -7.25348 -17.48828 -13.47441 -11.69169 -12.49356  10.12094 -18.24067   0.83593  -8.77465 -20.56048
 [841] -15.75145  -6.60663 -13.69602   5.53261 -10.97067 -25.88300  -4.68172   4.11551  -2.09063  -6.97928
 [851]  11.54985 -24.14892 -12.99265 -15.05715 -26.35646  -9.49165 -17.55231  -7.54939 -20.51274  -7.29464
 [861]   2.24866   2.40184  -6.88408   0.55948  -7.41537 -27.59101 -24.01985 -11.17612 -24.59056  -9.98298
 [871] -18.94422 -19.27632  -5.69466  -6.43853 -11.47399 -15.06406   1.38486  -4.21842 -14.90408  -9.07737
 [881]   0.19532 -11.35078 -19.56560  -9.25288 -14.64436 -25.56167 -26.14363  -4.03614 -10.80604  -1.22518
 [891]  -0.62535  -4.00090 -16.10513 -14.76173 -25.49890 -12.74796 -22.71360   4.39479  -4.62313  -9.62846
 [901] -12.59034   0.55474 -15.82509  -2.62724 -19.19964  -5.90592 -14.00621  12.30464  -7.01888  -6.68624
 [911]  -3.50736   1.53301   4.75640   6.92010  -4.95960  -4.10576  -4.22065 -17.69833 -12.86365  -6.67419
 [921]  -9.60538  -9.87605 -15.24612 -26.54749 -14.44399  -5.50619 -12.49120 -15.58132  -3.07279  -5.67746
 [931]  -8.97394  -9.69684  -0.05886 -24.63628  -9.15756 -25.56488  -4.30014 -17.30616  -2.02380 -17.40122
 [941] -11.90749  -4.83646  -0.11739  -2.17642  -3.16044 -16.99101 -14.28239  -1.04174  -3.40694 -14.29376
 [951]  -0.13732  -2.34037   6.57413   9.01424 -10.70834 -20.99154 -18.96958  -4.44755  -0.81690 -20.88665
 [961] -22.00074 -11.59618 -20.25990   0.07973 -13.08686  -8.77299  -6.18519 -21.41187 -13.55368  -1.71078
 [971]  -5.08888 -12.10784 -19.59959  13.14861 -13.30264  -3.08658  -8.40427  -4.70066 -27.94099 -15.56069
 [981]  -7.88713  -7.30523 -24.39129 -21.29654   8.70596   2.21960  -4.43285 -10.80261  -3.24430   9.25269
 [991]   0.04128  -6.02727 -19.69384  -8.11925 -13.66681 -13.55660 -16.52119   7.44623 -29.79066  34.80182
 [ reached getOption("max.print") -- omitted 7000 entries ]
density_gramxlex <- density(pst_gramxlex)
plot(density_gramxlex, main = "Density Plot of pst_gramxlex", xlab = "pst_gramxlex values", ylab = "Density", col = "red")


pst_gramxsynt <- y_posterior$Gram_x_Synt
pst_gramxsynt
   [1]  -5.053453   6.187288   2.788221   1.284608  -4.870128  -6.332632  -3.613579   3.916330   7.211630
  [10]   7.799748  -2.643932  -3.106716   3.015732  -1.024004   3.825743   4.259845 -10.983481  -9.339822
  [19]  12.959679   6.541848   8.628574 -19.175502  27.641577  -1.544770   6.282753  -0.735286  -0.473835
  [28]  -6.199372  13.784847  -2.840982   8.067476  -7.237985  -8.564921  12.606500   5.555969  11.344557
  [37]   5.943935  10.006514   3.015910   3.455928  11.921744  12.284612   0.308969  19.797172   1.918032
  [46]   0.145443   3.852571   6.191142   1.228013   1.174749   0.247189   0.102489  -6.936929   0.878073
  [55]  11.062615  16.392734  11.407588  28.730324  -4.287731  12.664581   4.504808  13.951294  -2.830624
  [64]  -2.165854 -21.729867  -1.512891 -16.866201  13.026079   2.497641   5.595442  -0.875782   1.781424
  [73]   9.637527   0.631284 -12.888804   6.355983   9.994239  13.456519  -8.583225   4.242755  -4.019064
  [82]   3.189612  -8.103400  26.863973  -9.095801  19.069311  -8.410639   4.441780  15.847373  -3.726269
  [91]   6.071552   6.551298  -0.606627  -4.319927 -20.682455   5.685297   4.973333   1.660707  -6.152877
 [100]  -3.719542  16.713791  20.845728   7.773434   8.382857  14.904498  -0.605131  17.400956   9.785776
 [109]   3.597514  10.888214   5.469694   2.483437  -6.577348   0.864946   9.195712  14.132052   7.406341
 [118]  -2.393484   5.079770   3.961647   4.042530  15.335220   3.774312  10.009882  12.582455   5.485260
 [127]   2.045588  -7.816798   3.562309   9.432307  18.465854  -2.486680  -0.228495  13.930571  12.628623
 [136]  19.392177   7.757399  21.378542 -11.067366  -7.494346   2.371555  -6.540370  -6.033924   7.914687
 [145]   5.101072  19.699693   9.704806  -4.235995   4.355751   2.787770   9.549229  -4.539028  17.531405
 [154]  20.284718  12.329194  -2.706850   8.241642   7.627439  19.492538 -23.548634  -1.004127   2.284058
 [163]  11.915772   2.532465  -7.843447  10.366107   3.604952  22.241983   2.027551  12.019639  -2.149810
 [172]   0.955109   3.482669   0.663005   8.796421  -9.734590   8.506266   5.484685   6.227016   2.697402
 [181]  19.287776  19.861910 -11.175707   6.049371  22.623906   9.995620 -10.211583  -0.271583   4.162394
 [190]  13.432062   4.207886  -2.112518   0.150159  -4.654543  -3.443783   5.245484  -2.259995   2.774097
 [199]   7.813589  14.812771  -1.988457   1.994787  -3.995156   3.473761   3.508531   3.455824  11.349462
 [208]   9.894377   5.403925  20.375832  19.608787   5.164285  -0.290915 -14.175791   2.050422  19.904954
 [217]  12.788805   8.102365   0.287716  14.407251 -10.444507   7.054463   5.198207   9.477967  14.179353
 [226]   4.684244  -4.266322   6.922544   8.215692   7.301607  20.094777  15.694471  15.993304  17.012609
 [235]  18.260354   7.328896   4.283168  -2.291487   2.777631   0.775336   9.548955   6.559970  -7.379430
 [244]   6.325941  -6.431213  21.182812   6.218338   5.947482  18.640357  -1.351808   8.462403   6.992952
 [253]   3.664319  19.233249   2.899178   1.535741  25.139058   2.841079   1.213712   5.547729   8.783602
 [262]   6.908939  -0.091830   3.224392  -5.788026   2.826116   1.519525  -7.765002   8.493097  19.977919
 [271]  -0.486242   9.558352   2.002735   7.675204   8.752569  -5.093942   2.877616  15.409736  10.103208
 [280]   3.336653  -5.400225  -2.423008  -1.526734 -13.906285  16.009958  -1.162662  -4.959278  16.996247
 [289]  -2.019232   1.964645   2.759623   0.043366   1.327680  -8.706122  13.274514  -4.936797   2.659285
 [298]  -1.512589  -0.294874   6.404691  18.796568   9.428658   8.219358   8.899932  -3.546023  -9.899052
 [307]   1.965884   9.535152  -0.351653  -8.208483   4.568598   3.492569  -0.430720  -7.955663   9.799567
 [316] -21.958958   0.067696  27.516985  -2.688834   7.934065  12.272655   1.170769  -3.133768  -1.318990
 [325]   9.569706  14.969196  -1.956410  16.811704   0.160913   6.049986  -4.029243  10.554711   3.105128
 [334]   3.629912   9.884248   8.627549  10.816349  -5.030813  -5.997755  10.147918  15.785601  -1.311451
 [343]   7.133458   2.989098   8.884431  -2.347602  14.892991   2.421884   7.163049  -4.874137   1.305174
 [352]  10.627507  -4.155432   6.176532  -1.322800  14.024713   0.568358  18.763436 -12.333700  -1.006919
 [361]   1.977944   2.437857   2.098135  12.671817   3.213519  -8.483142   7.966549  17.372031 -10.508921
 [370]  -4.235374   2.550647   0.448104   3.902981   3.124080  -0.993473   5.760251  -0.548848  -7.340884
 [379]   5.584966  12.245191  15.573070  -6.598894  15.763066  23.671780   3.013174  -2.736038  -2.421748
 [388]  -5.660911  -4.268690   3.044785   2.422531  -1.704197   2.988869   4.077711   6.341435  -6.349027
 [397]  -2.251408  -1.811620   4.125453  18.976401   3.214969   3.074487   2.677881  -4.881523   1.833165
 [406]  18.040605   9.751776  13.333837  18.255246  -3.189583  -1.991010  13.039830   6.144306  16.125345
 [415]  -4.124313  12.892118   7.829574   0.954264  -3.821196  10.606780   9.064349   2.475111  -0.347580
 [424]  -8.557615   7.055253   5.629817  27.635489 -10.543477  -0.741416   7.692615  -0.222345  -3.245987
 [433]   3.267856  12.454894   9.440006  12.657657  11.303053   4.631319   0.902379   8.075658   5.818455
 [442]   6.288345 -13.256888   1.737642   9.517268  14.521608   5.182836  16.173184  -2.360418   5.721045
 [451]   4.986295  -7.084212   0.958797   6.155434  -5.333158  -2.041216   3.123794  14.143095   5.531897
 [460]  -2.620152  -5.702093  -4.931056  17.739409  21.886783  -5.449530   0.310195  21.781052  15.007412
 [469]  -4.696156  -1.432897  20.038847  15.436156  11.283308  -4.860775  -7.353670   4.733178   3.043015
 [478]   2.799513   9.834391   8.391123   1.277970  -6.122896  -4.962989  25.508084  -7.717435  14.207182
 [487]  17.838391  19.428274  -4.812144  -7.945381   2.251566   2.487607  -0.002364  12.629385  10.885681
 [496]  -5.232177   7.663254  19.422900  -1.059164  -8.704393   0.159366  23.095980  14.822701   1.832953
 [505]  -5.953754  12.345220   6.261403  17.237732  -0.734317  14.811900  28.598327  12.981526  -9.962202
 [514]   8.513484  -0.889745  16.373108   9.158000  -3.408188   7.680220  -9.363283  16.090447  10.043217
 [523]   2.746883  23.472436  12.805661 -12.517864  -6.557431  23.705527   9.005702   1.948991  24.532423
 [532] -14.184604   5.817268   7.035089  -1.496171   5.874163   2.668661   3.817875   5.918923   4.632779
 [541]   3.571793   5.316943  -5.335769  16.851643  19.601852  11.295398   9.688587 -14.697673  14.310540
 [550]   3.202705 -12.199866  -8.082167   4.922504   5.447594  15.396581   9.833449  25.784216  12.717465
 [559]  -2.471140   1.368212 -12.856060  -1.141232   5.831997  11.831274   6.964628  13.222256   1.826584
 [568]  -1.803269   1.739197  -7.391400 -19.676821   1.418664   6.170360  11.433315  18.679056  10.429916
 [577]   1.089153   7.766909   7.150399  13.463836   7.383111  -0.428451   8.986215  -6.312528  17.073858
 [586]  25.841547  16.451061   1.439090  -2.660281  -0.967140  -6.694129  21.065100  -7.786669  -2.479409
 [595]   2.377931  20.000263  10.938845   3.631645   7.303160  -8.328875  -0.654927  14.645951   6.273834
 [604]   0.522520  -5.995467  -8.103050   4.813131   1.636077   1.362243  10.534795  18.323837  12.507997
 [613]  -6.871509  13.402546  13.536620   5.503037   2.175402  -7.231038   1.415712  13.186322  10.548349
 [622]  16.419156  15.032837  -7.613518   6.620033   2.571275   6.914560   3.526747   2.140931  -1.237016
 [631]   5.929109  -2.538458   1.444144  -7.651389  -2.286604   8.428135  -4.935518   9.211989  -0.014994
 [640]  -4.247242   8.357060  17.159924   4.456236  -2.946552  15.916110  -5.383668   2.688395   8.151128
 [649]  -2.700233  12.350878  28.148408  21.185762  13.165640   1.125888  10.159383   3.054255   7.305171
 [658]   8.445932   6.896199   0.203805  12.987687   5.620717  15.626413  11.663436   6.877827   8.077279
 [667]   4.668451   0.015477   9.984810  -2.084865  11.760378  16.255061  11.815446   0.786651   9.726244
 [676]  -2.323269  13.767656  -6.573214  13.470867   1.563920   6.301512   5.006630   7.239981  10.116298
 [685]   7.797837   5.843406  19.191706  11.087234  25.614871 -15.980162  18.960213  12.199922  20.179291
 [694]   9.843779  -9.570758   1.042982   1.193888  12.653550   8.809818  13.095755  16.630396  -3.519898
 [703]   9.752238  15.176253  16.691420  -0.970269   2.526302   3.556606 -13.958326  11.634049   0.601352
 [712]   0.513944   1.784148   6.963140  13.948034   3.989098   8.552681  -1.819524  -8.605711  10.568255
 [721]  -8.689244   3.296182   9.347971   9.579683  -4.996927  19.669514   8.214402  21.576987  -3.354634
 [730]  16.862911  -7.265758   6.733549  26.113003   2.400829  18.369396   7.820513  -1.301266   8.568795
 [739]  -4.268840   0.641531  20.325749  19.461683   6.121038  -4.318052  12.716037   2.740823   3.760392
 [748]   1.757774   0.017462   7.169518  12.714414  -1.683254 -17.584983  16.546308  30.359976   5.410195
 [757]  -1.328611   0.983844  17.968225   3.955490  -3.198009  11.150399  17.582791  11.933850  13.303401
 [766]   3.346180 -14.141724  15.863480  14.580182   7.442435 -16.832083  15.571718  10.980115   9.057285
 [775]  12.223758  22.166146  18.468949   5.159932  -2.596826   0.177323  17.546398  -2.340819 -10.739001
 [784]  13.851711   8.649524  -0.676625   1.916790  -0.230504   4.486365   3.497703  -0.974030  -8.302877
 [793]  -9.957042  15.014614  -5.888460  -4.721529   1.183185   0.888209  16.190886  -1.503265 -13.911138
 [802]  11.977848  12.100279   6.292232 -14.023810  -3.759534 -12.159657   1.121957  -6.947301   1.256539
 [811]  10.867987  17.357463   8.429953  -1.639224  13.357072  -9.569509  -1.543032  19.295377  14.068746
 [820]  14.481696   2.777179  11.448611 -15.947981  11.140153   7.911482  -0.914805   8.657599   9.579986
 [829]  -1.804865  -9.815804   7.580361   8.306247   2.065076   8.426913  19.803284  -8.565734  12.120181
 [838]  -2.147138   3.198560   7.143374  10.880711  10.227392   1.402729   0.764356  11.930294  12.809317
 [847] -16.394427   2.641887  18.490961  14.833648 -18.337915   7.110666   4.970343  14.513695  13.699080
 [856]  -7.290834   1.049503  17.622133  -7.504536  -3.722740   8.276255  -5.210400  15.445376   5.436044
 [865]  -2.251778   7.757196   5.234596   2.733717  27.007857  25.268541   4.065656  11.640994  12.830218
 [874]   5.452132  -6.976616   2.417533  -0.062211   3.390345   4.458874  -5.175640   1.479404   6.089584
 [883] -12.033990   1.891364   7.365701  25.115510   7.663163   2.599787   1.056858  10.370422   7.933016
 [892]  10.187097   2.440983  12.307552  14.815614   2.486874  12.888974  -2.736984   4.362567   2.402114
 [901]   9.870973 -25.497400   3.613346   1.818221  14.448089  -7.433200  16.808825 -10.656762   1.349171
 [910] -13.792504  11.097103   6.380533  -4.403192   5.192574   3.279144   9.336076 -14.284250  13.425168
 [919]  23.041343   3.478552   6.070094  -9.637628  12.883459   8.688329  16.091607 -18.836069  13.398496
 [928]   5.240835  11.189526  -1.539839   5.668806   5.575354  -6.911831  17.712669  -2.867567   6.120091
 [937]  -2.833317  12.702545 -17.544912  10.438811 -18.340939  -3.066357  -3.701081   5.496427  -7.708764
 [946]  10.176771  -0.404156   3.491030   9.951289   0.125318  -0.950743   1.744051  -4.739348  -0.859759
 [955]  -0.234398  10.636048   2.310768  -9.338066  -1.976172  12.334621  19.533671   5.814421  20.861448
 [964]  -7.207150  12.383274  -2.055910  -2.315557  11.003435  -4.295582  -3.113120  -1.110859  10.263287
 [973]   5.595698  -5.453013   5.098184  17.708365  -6.872907   4.302581   4.022909  12.102674   6.063307
 [982]   8.300545  13.738784  -7.712485  -2.883992  -3.279923   0.957886   1.273057   0.522583 -14.027584
 [991]   1.021488  -8.075856  14.415910  12.664000  13.644429   9.483391  -3.101986  -7.135397  20.942074
[1000] -19.067728
 [ reached getOption("max.print") -- omitted 7000 entries ]
density_gramxsynt <- density(pst_gramxsynt)
plot(density_gramxsynt, main = "Density Plot of pst_gramxsynt", xlab = "pst_gramxtyps values", ylab = "Density", col = "red")


pst_gramxgenxlex <- y_posterior$Gram_x_Gen_x_Lex
pst_gramxgenxlex
   [1]   0.620247 -14.802705  -3.031907 -12.518206  -3.485587   0.896144   3.107528  -9.625840 -13.807638
  [10]  -1.854760  -7.190642  -7.530326   0.490543  -1.693198  -5.949240 -12.643799   1.944658 -15.874529
  [19]   3.689906  -6.660178 -12.844383  -5.479248 -15.729088   0.009928 -11.152186  -3.481186  -3.005218
  [28]  -3.888231 -12.468471  -6.519007  -3.542226   7.375220  -6.029863  -2.450335 -20.426812   8.361814
  [37]   0.529527   3.802957  -6.831232   3.449239 -13.695477  -1.145402  -2.135542  -8.899339  -0.836994
  [46]  -0.771729  -2.103416  -7.061291   3.513063  -1.325883  -0.709435  -2.653010  -1.246442 -12.914394
  [55]  -7.805561  11.540513  -5.995020  -9.074787  -8.644881  -3.211989   7.426858   9.116633  -1.826742
  [64]  -0.390749  -1.556447   2.437535   5.455385 -14.371282 -11.949033  -0.511778   4.446197   1.861604
  [73]   5.296238  -3.349326  12.734896   6.279348 -22.098260   5.885467   0.218703  -9.375201  -6.177203
  [82]   7.697090  -3.380415 -14.354835  -7.561720 -13.500687  -5.518803   0.675018  -8.747222  11.838791
  [91]  -2.414711   1.920746  -3.534129  -2.534596  -9.506622   2.559798  -1.210134   8.740015  -3.730318
 [100]  -0.122586   0.848465  -1.815424  -7.862387   5.037356   3.227346  -2.880093 -13.556299 -10.254845
 [109]  -3.132556  -6.643010   7.716824  -0.550454  -9.380685 -21.776045  -9.193138  -8.522697  -1.903133
 [118] -10.035891  -9.191380  -5.797914 -14.693970  -8.906908  -8.433130 -17.574949   1.157918 -13.687884
 [127]   6.127547  -8.087820  -8.331359  -7.939487  -6.316624  -8.693443 -13.065221  -0.032911  -4.646264
 [136] -13.532414 -13.042837   4.320586  -6.099963   5.181466 -16.534965  -8.978441  -5.049911  -3.781763
 [145]  -6.533067 -11.388316  -4.281225  -7.149702  -0.079692  -8.094843  13.883965   6.765797 -11.511591
 [154]  -1.593876  -9.263760  -8.760471   3.527212 -11.224924 -10.512672  -9.557601   6.804476  13.843517
 [163]  -3.770277  12.634594   5.994477   0.637912  -4.788076  -8.656035  -5.239882 -11.096487  -7.157884
 [172]  -0.940722  -3.697434   0.237897  -5.290765  -6.351777   1.743446 -17.383667   5.192458 -11.139472
 [181] -12.132820  -2.259982   1.072937   1.658730  -4.300352   0.312188  -3.566758  -4.117766  -6.701533
 [190] -15.218034 -11.657324  -2.316665   3.272414  -3.142184 -10.935175  -3.780663  -6.115197  -7.506022
 [199]  -4.673553  -4.892705 -11.357990  -3.414878 -17.888750   0.031883  -4.542737 -11.361098  -1.794212
 [208] -10.322020   5.493849  -7.137931  -4.120643 -19.275419 -13.902299   2.775271   9.619169 -14.791475
 [217]   4.955575  -9.697786   1.360200 -12.115047  -5.850588   2.633057  -3.563479 -14.094379 -15.521000
 [226] -12.055339 -15.700789  -6.863448  -1.660808   3.405629   1.177136  -4.861643  -2.159526  -5.515356
 [235]  -8.027649   3.253884   0.832141  -1.020184   0.832413  14.346098   0.139533 -15.566387 -15.308158
 [244] -13.857545  -0.760521   0.968491  -5.041949  -3.920126   6.415560  -1.559519 -13.264340   0.227163
 [253]  -9.847433   2.747456   1.009345  -6.985124  -7.978682  10.924207  -7.394256  -6.958987 -13.811044
 [262]  -2.468695  -0.589220   1.245843   3.451556  -4.446727  -1.401428   3.258065  -1.630284  -3.772275
 [271]  -1.073686  -1.971427   0.819016  12.077074   0.043719 -18.449167  -1.116916  -2.054095  10.499059
 [280]   1.414139  -7.979170 -11.205719  -3.404366   7.540113   2.597013  -0.538856  -4.046045 -14.701842
 [289] -11.756083  -2.314620   5.827013  -9.898007  -0.337264 -15.585084 -11.739887   3.904751  11.033448
 [298]   3.674742   7.910148 -10.836030  -6.354638 -11.746723 -12.538644  -0.464538  -3.512302  -7.818940
 [307]  -5.347117  -9.747777   2.704066  -6.636801  -4.850151  -0.773624  -9.638883  -4.601407   0.338220
 [316]  -0.568073 -16.862695  -6.571378  -2.161010  -6.857514   2.674314  -2.807941   7.253726   6.861018
 [325]   7.887984  -4.746066  -3.428606   4.525292  -9.257634  -4.931353  15.691386   1.859572  -9.213566
 [334]   1.739223  -4.021720  -3.396908   0.154625  -6.964899  -7.912046 -11.010715   3.250941  -4.521081
 [343] -11.078600   6.789434  -6.470014   3.413213  -0.918719  -2.771844  -6.666989  -1.341112 -14.986580
 [352]  10.804664  -5.575929  -5.016929 -13.003689 -16.685012  -8.112235  -7.279620   3.898139  -6.951367
 [361]   0.550031  -4.361964 -10.743320 -11.609663   7.678704   2.556417 -16.102261   8.165762  -3.306431
 [370]  -5.959541 -19.765213 -11.691467  -2.698805   4.660633  -1.332568  -8.533394  -2.648368  -3.944726
 [379]   5.644252  -8.760918   9.131497  -2.281991   0.132333   6.395759  -6.148555  -0.205315 -11.693807
 [388]   0.798298   0.297853  -3.428946   1.457795  -6.462318  -9.768840   7.230486 -13.353924  -0.558339
 [397]  -4.114742 -12.762576   1.326561  -0.582889  -3.092732  -0.701296  -4.695475  -5.043239   4.314954
 [406]  -6.798533 -14.609792  -2.832972 -16.915572  -6.464366  -1.062764  -5.423406   9.933683  -1.605776
 [415]   8.854594  -8.552998   1.246873   3.981942 -17.461225   4.375500  -0.886104  -0.234374 -13.841314
 [424] -14.584387  -1.061976   2.030452  -4.701442  -7.712958  -5.425253 -10.510626 -16.268405 -15.952177
 [433]  -2.722489 -16.425928 -14.459630  -1.417801  -1.403630  -8.529098  -0.173019  -0.901668  -7.299485
 [442]   2.146924  -1.518157  -2.488474 -16.883947 -10.479583 -16.197317 -14.640758  13.534771   3.775491
 [451]   4.781548  -6.435578 -11.065854  -5.890799   2.348578  -5.801838  -0.222637  -7.981581 -14.688852
 [460]  -2.648336  13.263132 -23.656847  -6.076678   7.334213   4.761573  -3.294466 -12.659610 -15.464786
 [469] -19.903876  -1.075635  -2.510406   4.187656  -0.524530 -19.234291   3.769031  -3.038032  -3.170268
 [478]  -6.977247  -5.689943 -13.613682  -5.219825   6.138564 -21.760126  -3.512074  -2.348567  -6.430784
 [487]  -4.208613  10.787802  -3.597537  -6.235739  -0.255110   2.796509  -3.524978  -1.589955  -5.537785
 [496]   0.100448   4.507430 -14.027323 -14.718673  -5.032210   3.978935  -8.415242  -7.598378   1.895633
 [505]  -2.688044  -6.428093  -4.079578 -14.655119  -5.042970  -2.398924  -8.967551  -9.880963   7.086559
 [514] -21.782349  -7.402374   4.107432   9.831590  -6.017419 -13.817527 -10.089797   4.159070   2.309493
 [523] -11.588469 -21.693938  -1.732658 -15.415267  -5.529209 -10.513573   2.553482   3.458083 -17.926387
 [532] -13.505860  -2.921896 -10.185443 -18.835899  -4.003539  -2.009388 -16.684363 -10.872242  -3.111107
 [541] -13.939475  -1.871000  -2.494939  -4.722964   8.201888   3.715337  -4.798800   0.258261  -1.044373
 [550]  -2.750095  -7.466510  -0.223540  -4.301676 -11.833573  -7.805759 -12.561109 -19.545580  -7.381407
 [559]   2.678297  -5.632948  -2.656473  -2.637534   4.558934 -10.700316  -2.827868 -11.557701  -3.334698
 [568]   0.891357 -10.445311  -6.289334   7.428145  -7.536621   5.639228   0.706883  11.864298   0.475986
 [577]  -8.269394   5.121606  -8.274295   8.340773   6.578011   3.540447   6.742491 -10.150563   8.266405
 [586]  14.895748   2.355100 -13.025111 -11.977957   1.785317  -6.939888  14.294423  -6.984764  -7.309003
 [595]  -4.470453  -6.653162  -8.122173  -6.006874 -11.890401   3.719309  -6.045160   1.322056 -14.132577
 [604]  12.173284  -6.271259   0.027098   2.807536  -2.469541 -12.838001   2.098926  -0.550622  -3.390257
 [613]   2.293279 -10.542020 -11.961513  -7.940719   1.524430 -10.606955   7.423870  -9.847959   9.749779
 [622]  -4.108178  -1.253261   0.504294 -13.007417  -7.862513 -11.729238  -4.161678 -18.402110  -5.371164
 [631]   2.952858  -9.793527   1.279108   0.111887  -2.708746  -6.389017   8.564700  -6.090507  -1.982940
 [640]  -0.056550 -12.234259  -6.980690 -11.297596   2.548561  -3.971944  -6.606962  -1.905842  -6.196378
 [649]   6.947096 -12.748760  -1.698266  -4.434041   0.710141 -17.179844  -5.692807   2.992174 -17.428397
 [658]  -0.703328 -21.199391   1.820945 -16.522415  -7.330906   1.322582  -6.973663  -8.249427   5.551910
 [667] -11.164655  -8.355179 -12.989436 -11.239530  -4.962034  -2.523621   6.680256  -4.346330  -1.784670
 [676]  -9.746036   0.650990  -4.858280 -13.398616  -3.227625 -20.024307   4.928818   2.051492  -9.795434
 [685]   1.758038  -7.733281  -5.729714  -0.218997  -2.508496 -12.625296  -4.992942 -11.189414  -5.706230
 [694] -14.084361 -13.014068  -0.065583   0.636258  -0.141185 -10.170749 -11.234265   2.953423   1.329771
 [703] -11.147428  10.515368  -8.082523  -7.381537  -6.291954  -3.211842   0.015421 -13.156592 -11.252602
 [712]  -3.962140  -5.167692 -13.131789  -8.307483  -0.241687 -10.897203  -4.320337 -19.007264  -9.577169
 [721] -14.657248   5.045017  18.912244  -1.727998  -3.725317  -5.275010 -22.654108 -23.193689  -4.315390
 [730] -11.701336   5.594444   3.502378 -17.652708   8.043162  -4.120286  -7.030010  -4.659263 -13.517549
 [739]   0.710200  -1.406357  -6.035634  -5.589002 -23.326234  -3.485686 -11.066429  -7.620707   0.700913
 [748]   9.560767   4.243233  -2.213589  -4.511416   7.236957  -2.053329 -13.815921   4.200006   0.875215
 [757] -14.897697  -1.585937 -12.997388  16.312842  -2.237487   5.670193 -10.471271  -8.923421  -8.081807
 [766] -13.640058  -9.687109  -7.663081  -2.202857  -0.378754 -14.335197  -6.224559  -9.500599  -3.842366
 [775]  21.742485  -3.833665   0.041075 -12.865040 -18.699726   2.257961   1.244823  -8.128887   3.039698
 [784] -11.388324  -0.970902 -18.074340  -0.319527  -7.506121   4.585576  -8.159320  -1.078308 -10.832106
 [793]  -0.114377   3.316085 -12.055526 -16.876832 -11.402611 -10.071800  -3.832329  -7.967286   7.247344
 [802]  -8.872758  -4.576918  -5.058670 -12.051726   2.368051  -9.033369  -3.231642  10.854386 -16.697554
 [811]   5.347236   7.596276   0.823617   0.481892 -15.743097  14.633736  -7.207215  -8.574181   1.891811
 [820]   1.331926   9.720149  -8.724017   6.995028 -12.211293  -8.219172  -0.417589   5.553114  -2.569631
 [829] -10.700535   5.074061  -6.909419  -7.107369  -2.292046   3.892487 -13.122155   5.295286   2.367691
 [838] -23.526865  -3.160103   1.970745   5.672533  -9.851683  -1.593009   2.264230  -4.184854  -1.724977
 [847]  -5.957402   5.330426  -7.144102   4.831142  -5.056589 -13.649883   6.151373   0.349801 -16.250039
 [856]   0.345436  -7.257334  -7.457765  -1.874905  -6.147608 -14.035478  -2.524430   2.267094  -4.610564
 [865]  -4.060788  -5.217743   2.820399  -9.386830  -5.061692  -6.484957  -7.391064  -7.197869  -0.749346
 [874]  -8.427933 -18.900975   5.392020 -13.569570   0.487969 -12.382680  -4.323395  -3.870834 -13.129241
 [883]  -3.146504  -8.023497  -3.402560  -0.553354  -1.378686   0.870250   1.184436 -10.125064  -1.750484
 [892] -13.080093  16.033518  -6.960667   5.849638  -2.272554  -5.575062   1.845667  -9.831104 -14.875070
 [901]  -8.224598  13.417903  -7.788852  -1.262667  -9.791590  -2.572758   0.453520 -12.982236  -1.728957
 [910]   1.275073  -7.771717  -5.098038 -17.639456   5.706840  -4.571080   8.393079  -6.789773  -9.943346
 [919]   1.917187  -8.628855  -2.836402   0.030583 -20.947980   3.680629 -17.749991  -5.685082  -8.634569
 [928]  11.445404   0.731374  -2.867261  -4.983436  -0.509161   5.714592  -3.955416 -11.137285 -12.551951
 [937]  -5.851357   2.959546  12.469716   4.355750  13.032723 -13.835589   2.937336  -3.664231  -5.912822
 [946]  -8.453695  -1.902683   2.053226  -4.158862 -17.211593   2.936978  -9.298263 -10.498156  -8.288327
 [955] -12.637861   6.639447  -8.961019  -6.739150  11.067115  -9.019571  -3.377147  -6.240592 -24.084999
 [964]  -9.665404  11.307511 -11.462502  -7.081446  -5.074047  -6.726880 -15.785986  -7.846104   5.508417
 [973] -10.285089   1.042023  -4.616004  -0.390450  -5.443724  -7.790874  -3.705020  -4.215645   2.809870
 [982]  -7.632529 -15.194150  -1.630142   1.825785  -4.753468   0.171565  -4.908247   4.886107   6.316514
 [991]   3.432370 -15.116581  -5.825768 -10.343139  -4.566267   5.458148  -6.620336  12.883634 -16.788879
[1000]  -7.662392
 [ reached getOption("max.print") -- omitted 7000 entries ]
density_gramxgenxlex <- density(pst_gramxgenxlex)
plot(density_gramxgenxlex, main = "Density Plot of pst_gramxgenxlex", xlab = "pst_gramxgenxlex values", ylab = "Density", col = "red")



pst_gramxgenxsynt <- y_posterior$Gram_x_Gen_x_Synt
pst_gramxgenxsynt
   [1]   4.79680  -0.66366  -3.95663  -4.53607 -14.82178   4.06552  -6.11669  -1.67869 -11.10258  -6.52136
  [11]  -0.25257  13.35644   2.60505  -0.46508   3.55198  -6.75014   9.43359   9.58306  -2.80645   3.93057
  [21]  -1.64812  -7.75411   7.18887   4.42505   9.85447  12.74543  -9.65229  -0.22764  -3.49437 -12.80035
  [31]   4.19057 -12.34624   6.74215   1.19688   4.13037 -14.24327 -13.89912   4.96395   3.73231   5.50308
  [41]  -1.85232 -10.82491  -7.62924  -8.10522   5.43214  -9.51251   4.44598 -12.86420   7.68843 -18.03039
  [51]   0.54491   4.68682 -21.63700  11.51606   7.53301 -15.12835   2.45170   4.69205   1.50995   3.68025
  [61] -17.30413  -3.00069  -6.05521   3.53660  -1.28171   3.83999  -2.05078   2.27652   4.53554  -8.74292
  [71]   3.06209 -14.22254  -3.87473   2.04190 -14.64583  -1.08543   7.59515  -6.37053   5.03760   4.11445
  [81]   5.98911  -8.05926   0.85425  17.32783  -2.16369  -6.00804  -8.36117  -4.54220   0.10815  -3.33501
  [91]   0.83863   0.82968  -3.56228   2.27058  16.12058  -9.89188  -0.88719 -12.44936  -7.86365 -10.02252
 [101]   0.80040   0.45695  -7.85692  -5.55920 -16.45942  -5.03425  -2.28429  -3.32452  -7.38056   1.79528
 [111] -20.40262  -8.00939  11.22045   1.97417   4.56876  -2.59250   0.88417  -1.52974   2.22934   2.44286
 [121]   3.08602  -6.75664 -14.59565  -4.20762 -10.80472  16.42758   3.44389  -8.89697   4.43523   0.89889
 [131]   5.21822  -0.44863   1.27385   3.91946  -2.55928 -12.59955   6.67246  -3.21958   2.44467   1.04457
 [141]  10.48983   4.34259   6.24864  -0.52028   6.11994  -4.77286  -4.16854   5.03682  -5.83879   5.07564
 [151]  -0.81987 -10.70098  -2.86606   2.60383  -7.95193   4.86093  -4.83333 -15.83810 -12.07366  -5.20263
 [161]   7.44624 -16.31818  -3.09645 -16.26770 -10.79059  -3.05172 -14.59105  -2.87476   1.08893  -4.14071
 [171]  14.25532 -12.81575   3.44824   3.98420   0.24049   2.03289  -4.60779  -5.40431  -0.08470   3.37931
 [181]   0.79936  -2.33568  -3.45137   1.38088  -1.48262   6.91184   3.42439  17.58672  -4.54290  14.16279
 [191]   3.41243 -15.50567  -7.90779  18.78620   1.44027  21.15618  -0.08364  -6.43235  -3.23267 -11.37175
 [201]  11.66050   6.41293   8.22507   0.80143   3.01132   6.56451  12.18455  11.04810 -11.89542  10.32697
 [211]   4.92790  -1.01939   0.84898   1.48878 -17.26581 -12.26544  -1.69592   2.97373  -3.36433  13.38861
 [221]  -1.22144   4.56913  -6.18935   6.79547   5.88359   5.76383   4.95729  -2.77201  -3.85408  -1.69244
 [231]   1.30238   0.04024   1.63689  -8.32265   9.19778  -9.07617   2.03961   0.90842   4.32316  -9.33032
 [241]   7.10762   2.40833   0.33314  -1.06807  -3.46095 -13.33281   8.71181  10.53903   7.64933  -8.07725
 [251]  -5.74637  -8.68838  12.50080 -17.49129   0.38741  11.44297  -8.35917  -4.68873   0.78127   0.80676
 [261]  -1.00959   4.70160   8.45713   2.51500  -2.72119   7.64757  -4.76983  -7.26742   1.05975  -2.00104
 [271]   0.59734  -3.54924 -17.41918 -10.93745   4.24897  20.16249  -4.38695  -0.30389   5.61923  -1.08673
 [281]   5.73644  15.96977  -2.29246 -19.48236  -9.35489   1.23592   8.91882  14.62063   9.07823  -2.54926
 [291]  -6.45629   3.27365   3.47729  -4.23807   9.52990  -2.99231 -14.37449 -14.74917   3.16050  -0.72516
 [301]  -9.34162  12.01793   2.65525   4.21371   1.73474  -2.06606  -8.63286  -6.96728   2.49832  -3.37411
 [311]  -6.97259  -0.40974  11.99833  -1.03509   2.21754 -15.24405   6.60547  -1.55384  -3.62616   2.89877
 [321]  -7.88314  12.55398  -9.74817  -7.33908  -6.84247  -0.65540  15.06684   0.88349   2.55417   2.80819
 [331]  -7.80754   1.43274  -0.13708   6.27036   1.56301  -6.72339   3.07820  -1.86643  -8.11209  -4.98292
 [341]  -4.53138   2.54747   9.45418  -1.04300  -2.88764  -0.84772   7.28758  -7.35568  -3.62747 -11.94777
 [351]   5.85402  -6.69356   4.42359  -3.06494  -2.09807   6.35945   3.08040   7.60341  -0.38057 -15.67417
 [361]  -4.01967   0.51797   3.93051  11.88993  -3.82948  -3.93018  -6.57109   3.48874   3.86306  -3.43056
 [371]   2.90849  -4.74979   2.78482  -0.21962  -1.24498  -1.03178  -2.81645   3.17303   0.74535  -5.97158
 [381] -14.38914  -7.55005  -8.72105  -7.75295   0.82800  -3.85458  11.13218 -13.45006  -2.15249   7.24786
 [391]   5.70858  11.15627  -0.10056  -6.62993  -2.99426   3.70457   7.52683   9.13529  -4.75849  -1.11798
 [401]   8.50455  -9.35037   1.89316 -11.36686  -3.09600   2.54759   6.05223   4.69461  -3.78791 -15.74405
 [411] -11.24229   1.50943 -11.94883   3.73002 -10.81085  -6.92926 -14.72166  -4.68388  -0.86850 -14.42751
 [421]  13.77758 -15.15254   1.60323   1.32665  10.84280   4.17600   5.54015  -1.92205   0.23147  -0.83056
 [431]  -5.33514  -4.37006   6.64579  -9.30778  -4.95591  -4.53308   4.75256   1.33908   4.61772  -8.62356
 [441]   4.56283 -12.59240  -1.31703  -0.36174   4.60961  -1.07131   1.04040  -4.08906 -12.06296   6.30353
 [451]   3.48078  -1.11259  -6.39971  -0.35451  -8.98864   1.59138  -3.50060 -11.71642  10.43516   8.18201
 [461] -14.58988   4.91710  -7.22259  -2.63883  -4.84113  -0.38368   0.74348   1.57662  -6.37795   9.08419
 [471]  -4.88120  -9.80800   2.75498  19.11648  -6.75040   0.61883   5.02318  -0.43750  -9.71998   4.55130
 [481]   4.65533  -5.95557  -1.64783  -8.95818 -11.83004   5.93306   5.08460 -16.00421  -2.47398   7.13274
 [491]  -8.62462  -4.26217 -10.50634  -6.34045   7.27456 -10.62809   5.30840  -4.74419  -4.76206  -3.74513
 [501]   4.30048   8.37244  -0.61434  -6.82440  -1.33141   4.52402  13.14806  -7.23795 -11.04717   1.27587
 [511]   7.85785   7.15648  -4.62579   8.79503   8.59337   0.13336  -6.58775  -3.35582  -3.78230   9.11007
 [521]  -4.32392   0.97297   1.95285  10.15643   4.41570   9.45688  -0.03124  -2.91259  -7.28229  -5.23713
 [531]   5.09551  13.69087  -6.15977   4.55086  -3.77403  -0.79941   3.65810   8.36591  -2.02657  -2.08390
 [541]  16.68501   0.66901  -7.79278  -4.69246 -14.12354  -6.02752   7.47830  -8.29074  -9.28136   8.18579
 [551]   0.69957   5.71629  -3.24319  -4.03836   4.66481  11.02112  15.17714  -8.18491  14.60219  -8.37051
 [561]  -0.99550 -10.31264  -0.61431  -1.03096  -5.04767   2.48936  12.98982  -1.93032  -1.56462  -1.24008
 [571]  -9.08483   3.98283 -17.34103 -12.39267  -3.44971  -2.68159   1.51564   2.20357 -12.29948   9.80752
 [581] -14.37618   2.80384  -5.00508   6.20246 -11.69552  -3.90801  -8.63978   4.46300   7.09745  -4.15487
 [591]   2.86024  -3.20448  -8.56900   7.81890  -2.72164   0.85900 -15.02990  -1.17635  -1.28095   5.78900
 [601]   1.37368   8.29555  -7.47849   2.42205   2.68630 -12.52266 -10.68540   5.87396  20.15916 -19.09497
 [611]   3.40496   4.03689   6.96458  13.44747  -0.84731   1.83747  -7.55059   9.95183 -15.07585   0.16447
 [621]   0.47122   3.57319  -0.57295   0.85751   6.31198  -7.64016  -1.20628 -10.47506   2.81206  -2.31528
 [631]  -2.18538   6.96322   3.85904  -3.08985  -0.78018  -0.44074  13.31577  -2.09426   2.37098  -6.95222
 [641]  11.54578  -2.28373   3.58547   2.83131  -6.96746   1.35476  -2.82577  -5.30719  -5.15738  -3.53513
 [651]  -2.27071 -10.81718   2.52873  10.78710   0.58995  -3.77394  23.90205  16.96941   3.63778  -2.37676
 [661]   4.14784  -1.45876  -0.51433   5.25121  -4.85009   1.68906  12.07853   9.17475 -13.22617  -5.58723
 [671]   4.53172  -8.36025 -12.79123  -3.94993  -3.17967   2.13141   0.04840  -0.92287   7.18717  -2.97405
 [681]  -1.87123   5.59712   4.57467  -1.25091  -8.36981  -4.14314  -3.03748  -3.68798  -1.76825   5.05743
 [691]   7.01455  10.62312   3.27583   7.43941  -7.90156  -2.49159  -6.62102  11.19564  15.33847   2.27724
 [701]  -0.65882   3.85682   3.41269  -4.56832  -6.15672   0.20299  10.66067   9.94390  -5.38442  -1.89620
 [711]   2.93187 -19.09909   6.09052  -4.67877  21.36651  -0.97886   3.55853   0.19822   1.28536  -1.12138
 [721]  -3.15654  -9.62092  -6.49594   7.67597   3.20844  13.17165  -9.12641   7.07396   5.16062   5.17990
 [731] -10.21281  -3.84346  18.85390 -16.67855 -11.72858  -2.01287 -14.50355  10.61636  -0.50898  -5.53342
 [741]  -3.16896  -5.39847  -0.30850   5.14996   8.68014 -11.91333   1.77128   6.94479 -15.46782  -7.30382
 [751]   5.19234  -0.62978 -10.11059  13.35301  -9.94152   6.53848   5.10064  -2.19794  11.87526  -7.81431
 [761]   4.22935   5.66007  -9.10613   2.27731  -2.38874   0.75511   5.04614  -5.88689  -4.47708  -6.93521
 [771]   4.62171  -2.98139  -6.55029   1.35318  -2.26634   1.23321  -2.42234  -5.58933   7.74951  -2.52626
 [781] -21.98034  -9.07733   6.65731   5.89711  -0.67201  14.94723  -1.72227   1.28336  -2.55218   6.27036
 [791]  -7.55694 -10.05438   2.33089  -6.47918  14.38109   7.97685  13.43863   5.72975  -6.27088  -5.12891
 [801] -10.38483  -2.65587  -0.17726   1.93406  10.61523  12.77508  -3.30400  -1.52994  -8.36522  -2.50204
 [811] -14.87248   1.15320 -15.47669   3.64903  -3.51109   3.38369  -7.70409  -9.33328  -9.56918 -11.96660
 [821] -13.59116   3.75524 -15.06012  -3.21806   5.01985   2.79399  -6.09129  -3.89004   5.43588 -10.99740
 [831]   0.62360  -2.50204   8.59965  -5.11086  16.02439 -15.36392   1.07489  -5.70692 -11.92035  -9.38351
 [841] -15.00852   5.40399  -1.38646  -9.20040  -3.10081  -5.03051  -1.81434 -12.80361  -1.57690  -0.82307
 [851] -11.53819  -4.31601   0.99441   4.08846   3.01525  -8.74020  12.03497  14.78479  -2.50670   3.19120
 [861] -11.08006   1.42757  -8.86845  -8.02188   6.35772   1.23401 -17.15380   2.29389   8.58714   4.69361
 [871]  -1.30780  -2.87619  -9.44154  -9.48911   6.19711  -3.78805   3.14122   5.54141  -6.09216   7.33715
 [881]  -7.71926   4.34560  -3.47014   0.26588  -5.69945  -2.89963  -1.61871 -16.69608  -0.15318  -3.50287
 [891]  10.97544   9.04545   7.95043  -2.78155   6.39848  -0.80594  -6.93367 -12.01238  -6.13132  -2.03949
 [901]  -6.47285 -15.51588  -1.01739  -4.42227   5.38898   3.56857  -4.56961  11.97815 -17.70165  -5.39138
 [911]  18.09184  -8.50665  13.94961   0.69198  -2.35002   1.94383   0.34171  -7.21741   0.20248  -3.41240
 [921]  -0.92581   4.21961   5.49025 -15.20761   8.80558  -0.14488  -7.59720 -11.04855 -11.52112   1.39354
 [931]  11.44962  -1.62357  -9.56391   5.19771   2.46518  14.23173  -1.50831  -1.03227  -3.22814  -1.73574
 [941] -18.99407  -0.95427  -5.46847  -3.97505 -17.13381   3.04102  -1.84459  -4.29846  -8.07855   7.89818
 [951] -11.65176   9.96206   1.10124   3.18137   1.55294  -9.91283  -2.05961  11.92111  -2.51286   7.59277
 [961] -18.84041  -2.71859  12.91002  -1.14595  -9.13255  -2.63204  -7.55096  -0.76148   4.96732   4.45690
 [971]  -8.44975  -1.66066  -2.84893 -10.61449  -1.30864   4.70363   5.29430 -10.89069   9.64045   1.55495
 [981]  -8.34681  -8.32832  -0.16497  11.22528   0.58928  -4.96090  -7.47640  10.39034  -5.72071  -2.39909
 [991]   0.03738  -7.35270   1.91139 -12.91750   7.07267 -15.27261  -8.61871   1.12946  -1.71334  -3.11270
 [ reached getOption("max.print") -- omitted 7000 entries ]
density_gramxgenxsynt <- density(pst_gramxgenxsynt)
plot(density_gramxgenxsynt, main = "Density Plot of pst_gramxgenxsynt", xlab = "pst_gramxgenxsynt values", ylab = "Density", col = "red")



# check posterior predicts --> the fits looks very good --> suspision of overfit? --> cv validation?
predicts <- y_posterior$Predict_rt
dim(predicts) # 8000 x 1271
[1] 8000 1771
y_true <- contr_et %>%
  filter(AOI_id == region) %>%             # Filter by the specified region
  filter(!is.na(.data[[meas]])) %>%        # Filter out NA values for the specific measure
  pull(.data[[meas]]) 

ppc_dens_overlay(y_true, yrep = predicts[1:200, ])

compile model results

stats_df <- data.frame()

# regions <- c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions <- c("R3")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")

for (meth in methods) {
  # Loop over each measure type to read the corresponding model and extract data
  for (region in regions) {
    for (meas in measure_types) {
      model_path <- paste0("models/", meth, "_", meas, "_", region, ".rds")
      m1 <- readRDS(model_path)
      # print(summary(m1))
      # Extract posterior distributions
      y_posterior <- extract(m1)
      intercept <- exp(y_posterior$beta[,1])
    
      betas <- c("b_0", "b_Gram", "b_Gen","b_Synt", "b_Lex",
                 "b_Gram_x_Synt", "b_Gram_x_Lex", "b_Gram_x_Gen_x_Synt", "b_Gram_x_Gen_x_Lex")
      posterior_samples <- list(intercept, y_posterior$Gram, y_posterior$Gen, y_posterior$Synt, y_posterior$Lex,
                                y_posterior$Gram_x_Synt, y_posterior$Gram_x_Lex, y_posterior$Gram_x_Gen_x_Synt, y_posterior$Gram_x_Gen_x_Lex)
      
      hpdi_95 <- lapply(posterior_samples, function(x) hdi(x, credMass = 0.95))
      hpdi_89 <- lapply(posterior_samples, function(x) hdi(x, credMass = 0.89))
    
      # Prepare the results data frame
      temp_results <- data.frame(
        method = rep(meth, length(betas)),
        region = rep(region, length(betas)),
        measure = rep(meas, length(betas)),
        beta = betas,
        bval_mean = sapply(posterior_samples, mean),
        crI_95_lower = sapply(posterior_samples, function(x) quantile(x, 0.025)),
        crI_95_upper = sapply(posterior_samples, function(x) quantile(x, 0.975)),
        crl_89_lower = sapply(posterior_samples, function(x) quantile(x, 0.055)),
        crl_89_upper = sapply(posterior_samples, function(x) quantile(x, 0.945)),
        hpdi_95_lower = sapply(hpdi_95, function(x) x[1]),
        hpdi_95_upper = sapply(hpdi_95, function(x) x[2]),
        hpdi_89_lower = sapply(hpdi_89, function(x) x[1]),
        hpdi_89_upper = sapply(hpdi_89, function(x) x[2]),
        bval_median = sapply(posterior_samples, median)
      )
    
      # Append the temp_results to the stats_df data frame
      stats_df <- rbind(stats_df, temp_results)
    }
  }
  
}

# View(stats_df)

stats_df <- stats_df %>%
  mutate(across(
    where(is.numeric),
    ~ if_else(measure %in% c("FPReg", "RegIn_incl"), round(., 3), round(., 0))
  )) %>% 
  mutate(
    annotation = paste0(round(bval_mean, 2), 
                    " [", round(crI_95_lower, 2), ", ", 
                    round(crI_95_upper, 2), "]")
  )
write.csv(stats_df, "./stats2/stats_bayesian.csv", row.names = FALSE)
regions <- c("R2", "R3", "R4", "R5")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
groups <- c("gender_match", "target_gender", "lex", "synt")

all_diffs <- data.frame()

# Loop through each region, measure type, and group
for (region in regions) {
  for (meas in measure_types) {
    for (group in groups) {
      
      # Calculate mean and difference for each subgroup
      summary_stats <- contr_motr %>%
        mutate(lex = if_else(type=="stim_verb", "v", "a"),
               synt = if_else(type=="stim_adj", "in", "ex")) %>%
        filter(AOI_id == region) %>%            # Filter by region
        filter(!is.na(.data[[meas]])) %>%       # Filter out NA values for measure
        group_by(.data[[group]]) %>%            # Group by current group variable
        summarise(mean_value = mean(.data[[meas]], na.rm = TRUE)) %>%  # Mean of measure
        summarise(diff = diff(mean_value))      # Difference between the means
      
      # Append the results to all_diffs
      all_diffs <- rbind(all_diffs, 
                         data.frame(
                           region = region, 
                           measure_type = meas, 
                           group = group, 
                           diff = summary_stats$diff
                         ))
    }
  }
}

all_diffs

PLOT

measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")

# prepare motr for plotting
motr_plot <- contr_motr %>%
  dplyr::select(item_id, type, target_gender, gender_match, word_nr, word, AOI_id, subj_id, cond, gaze_duration, go_past_time, total_duration, FPReg, RegIn_incl) %>%
  mutate(region = as.double(substr(AOI_id, 2, 2))) %>%
  mutate(synt = ifelse(type %in% c('stim_adj'), "Internal", "External"),
         lex = ifelse(type %in% c('stim_verb'), "Verb", "Adjective")
         ) %>%
  drop_na(total_duration) %>%
  gather(measure, value, measure_types) %>%
  filter(region %in%c(2, 3, 4, 5)) %>%
  drop_na()

# View(motr_plot)

motr_lex <- motr_plot %>%
  group_by(lex, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(lex, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(lex, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>%
  ungroup() %>%
  mutate(lex = factor(lex, levels=c("Adjective", "Verb"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Lexical Category",
          method = "MoTR") %>%
  rename(type = lex) 

motr_synt <- motr_plot %>%
  group_by(synt, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(synt, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(synt, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>% 
  ungroup() %>%
  mutate(synt = factor(synt, levels=c("Internal", "External"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Agreement Type",
         method = "MoTR") %>%
  rename(type = synt)

# plot et data for plotting 
et_plot <- contr_et %>%
  dplyr::select(item_id, type, target_gender, gender_match, word_nr, word, AOI_id, subj_id, cond, gaze_duration, go_past_time, total_duration, FPReg, RegIn_incl) %>%
  mutate(region = as.double(substr(AOI_id, 2, 2))) %>%
  mutate(synt = ifelse(type %in% c('stim_adj'), "Internal", "External"),
         lex = ifelse(type %in% c('stim_verb'), "Verb", "Adjective")
         ) %>%
  drop_na(total_duration) %>%
  gather(measure, value, measure_types) %>%
  filter(region %in%c(2, 3, 4, 5)) %>%
  drop_na()

et_lex <- et_plot %>%
  group_by(lex, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(lex, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(lex, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>%
  ungroup() %>%
  mutate(lex = factor(lex, levels=c("Adjective", "Verb"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Lexical Category",
         method = "Eye-tr.") %>%
  rename(type = lex)

et_synt <- et_plot %>%
  group_by(synt, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(synt, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(synt, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>% 
  ungroup() %>%
  mutate(synt = factor(synt, levels=c("Internal", "External"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Agreement Type",
         method = "Eye-tr.") %>%
  rename(type = synt)

motr_et_plot <- rbind(motr_lex, motr_synt, et_lex, et_synt)

stats_df <- read_csv("./stats/stats_bayesian.csv", show_col_types = FALSE)

annotation <- stats_df %>% 
  mutate(region = as.double(substr(region, 2, 2)),
         measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob.")),
          method = if_else(method=="motr", "MoTR", "Eye-tr."))%>% 
  filter(beta %in% c("b_Gram_x_Lex", "b_Gram_x_Synt")) %>%
  mutate(Prediction = if_else(beta == "b_Gram_x_Lex", "Lexical Category", "Agreement Type")) %>%
  dplyr::select(method, region, measure, Prediction, annotation)
View(annotation)
plot_annotated <- motr_et_plot %>%
  left_join(annotation, by = c("method", "region", "measure", "Prediction")) %>%
  mutate(annotation = if_else(is.na(annotation), "", annotation)) %>%
  mutate(annotation = if_else(type %in% c("Verb", "External"), "", annotation))

plot_annotated
plot_annotated %>%
  filter(method == "MoTR") %>%
  filter(measure %in% c("Gaze Duration", "Go Past Time", "Total Duration")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction)) +
    geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = lower - 100, ymax = upper + 100), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point(aes(shape = type)) +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 50), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Reading time difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "External" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "MoTR",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = guide_legend(
      title = "MoTR",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid")
      )
    )
  )
Warning: Duplicated `override.aes` is ignored.
ggsave(paste0("./images/motr_rt_interaction.pdf"), device="pdf", height=6, width=8)
Warning: Duplicated `override.aes` is ignored.

plot_annotated %>%
  filter(method == "MoTR") %>%
  filter(measure %in% c("First Pass Regression out Prob.", "Regression in Prob.")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction, shape = type)) +
    geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = 0, ymax = upper + 0.2), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point() +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 0.1), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Regression prob. difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "Agreement" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = "none"
  )
Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).
ggsave(paste0("./images/motr_regression_interaction.pdf"), device="pdf", height=6, width=16/3)
Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).

plot_annotated %>%
  filter(method == "Eye-tr.") %>%
  filter(measure %in% c("Gaze Duration", "Go Past Time", "Total Duration")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction)) +
  geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = lower - 100, ymax = upper + 100), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point(aes(shape = type)) +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 50), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Reading time difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "External" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid")
      )
    )
  )
Warning: Duplicated `override.aes` is ignored.
ggsave(paste0("./images/et_rt_interaction.pdf"), device="pdf", height=6, width=8)
Warning: Duplicated `override.aes` is ignored.

plot_annotated %>%
  filter(method == "Eye-tr.") %>%
  filter(measure %in% c("First Pass Regression out Prob.", "Regression in Prob.")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction, shape = type)) +
    geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = 0, ymax = upper + 0.2), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point() +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 0.1), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Regression prob. difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "Agreement" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = "none"
  )
Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).
ggsave(paste0("./images/et_regression_interaction.pdf"), device="pdf", height=6, width=16/3)
Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).

---
title: "Data Analysis for Russian MoTR Reading Data and ET data"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r libraries, echo=TRUE, results='hide', warning=FALSE, message=FALSE, eval=TRUE}
shhh <- suppressPackageStartupMessages # It's a library, so shhh!

shhh(library( mgcv ))
shhh(library(dplyr))
shhh(library(ggplot2))
shhh(library(lme4))
shhh(library(tidymv))
shhh(library(gamlss))
shhh(library(gsubfn))
shhh(library(lmerTest))
shhh(library(tidyverse))
shhh(library(boot))
shhh(library(rsample))
shhh(library(plotrix))
shhh(library(ggrepel))
shhh(library(mgcv))
shhh(library(brms))
shhh(library(bayesplot))
shhh(library(tidyr))
shhh(library(car))
shhh(library(HDInterval))
shhh(library(gridExtra))
shhh(library(posterior))
shhh(library(readxl))
shhh(library(stringr))
shhh(library(loo))
shhh(library(MASS))
shhh(library(hypr))
shhh(library(designr))
shhh(library(afex))

shhh(library(coda))
shhh(library(rstan))
shhh(library(rstantools))

rstan_options(auto_write=TRUE)
options(mc.cores=parallel::detectCores())
rstan_options(auto_write = TRUE)
theme_set(theme_bw())
options(digits=4)
options(scipen=999)
set.seed(444)

```

# Read in ET Data
```{r ET-Data, echo=TRUE, warning=FALSE, eval=TRUE}
file_list <- list.files("/Users/cui/Documents/uzh/PhD/Projects/Russian_Agreement/russian_gender/ref/Eyetracking/", pattern = "*.xlsx", full.names = TRUE)
et_raw <- file_list %>%
  lapply(read_excel) %>%
  bind_rows()
```

# clean ET raw data
```{r ET-clean, echo=TRUE, warning=FALSE, eval=TRUE}
select_meas <- c("SFD", "total_duration", "gaze_duration", "FPFix", "go_past_time", "FPReg", "RegIn")

et <- et_raw %>%
  dplyr::select(IA_LABEL, item, word.id, list, RECORDING_SESSION_LABEL, SFD, IA_DWELL_TIME, IA_FIRST_RUN_DWELL_TIME, IA_FIRST_FIX_PROGRESSIVE, IA_SELECTIVE_REGRESSION_PATH_DURATION, IA_REGRESSION_OUT, IA_REGRESSION_IN, gender_match, part, target_gender, type, Region, condition, ACCURACY, animacy) %>%
  rename(
    word = IA_LABEL,
    item_id = item,
    word_nr = word.id,
    subj_id = RECORDING_SESSION_LABEL,
    total_duration = IA_DWELL_TIME,
    gaze_duration = IA_FIRST_RUN_DWELL_TIME,
    first_pass_fix = IA_FIRST_FIX_PROGRESSIVE,
    go_past_time = IA_SELECTIVE_REGRESSION_PATH_DURATION,
    FPReg = IA_REGRESSION_OUT,
    RegIn = IA_REGRESSION_IN,
    AOI_id = Region
  ) %>%
  filter(subj_id != "russ34") %>%  # russ34 has acc 0.6 according to the calculation below.
  mutate(
    go_past_time = as.numeric(go_past_time),
    SFD = if_else(first_pass_fix == 1, SFD, 0),
    gaze_duration = if_else(first_pass_fix == 1, gaze_duration, 0),
    go_past_time = if_else(first_pass_fix == 1,  go_past_time, 0),
  ) %>%
  rename(FPFix = first_pass_fix) %>%
  mutate(
         FPReg = ifelse(gaze_duration==0, NA, FPReg),
         FPFix = ifelse(gaze_duration==0, NA, FPFix)) %>%
  gather(measure, value, select_meas) %>%
  mutate(
    value = as.numeric(value),
    tgt_zero = if_else(measure %in% c("SFD", "gaze_duration", "go_past_time", "total_duration") & value == 0, F, T)) %>%
  filter(tgt_zero != F) %>%
  dplyr::select(-tgt_zero, -condition) %>%
  mutate(item_id = as.factor(item_id),
         subj_id = as.factor(subj_id)) %>%
  spread(measure, value) %>%

  # Note: we commented these lines out when running models because we logged the data and used mix effects to account for the variances and noises. If also filter outliers when running models, the results will not change qualitatively, but the estimated CI (or CrI) will be a bit narrower.
  # We filter outliers only for aesthetic reasons in plotting.

  gather(measure, value, c("SFD", "gaze_duration", "go_past_time", "total_duration")) %>%
  mutate(outlier = value > (mean(value, na.rm = TRUE) + 3 * sd(value, na.rm = TRUE))) %>%
  filter(outlier == FALSE) %>%
  dplyr::select(-outlier) %>%
  spread(measure, value) %>%

  gather(measure, value, select_meas) %>%
  mutate(cond = case_when(
    target_gender == "M" & gender_match == "Mis" & type == "stim_adj" ~ "a",
    target_gender == "M" & gender_match == "Mis" & type == "stim_verb" ~ "b",
     target_gender == "M" & gender_match == "Mis" & type == "stim_pred_adj" ~ "c",
    target_gender == "M" & gender_match == "Match" & type == "stim_adj" ~ "d",
    target_gender == "M" & gender_match == "Match" & type == "stim_verb" ~ "e",
    target_gender == "M" & gender_match == "Match" & type == "stim_pred_adj" ~ "f",
    target_gender == "F" & gender_match == "Mis" & type == "stim_adj" ~ "g",
    target_gender == "F" & gender_match == "Mis" & type == "stim_verb" ~ "h",
    target_gender == "F" & gender_match == "Mis" & type == "stim_pred_adj" ~ "i",
    target_gender == "F" & gender_match == "Match" & type == "stim_adj" ~ "j",
    target_gender == "F" & gender_match == "Match" & type == "stim_verb" ~ "k",
    target_gender == "F" & gender_match == "Match" & type == "stim_pred_adj" ~ "l",
    TRUE ~ NA_character_ # This is the default case if none of the above conditions are met
  )) %>% 
  # filter(animacy %in% c("Inanim", "inanim")) %>% 
  dplyr::select(-list, -part, -animacy) 

et 
```


# Read in MoTR Data
```{r MoTR-Data, echo=TRUE, warning=FALSE, eval=TRUE}
# The path to the data
data_path <- "./data/"
data_names <- list.files(data_path)

# Read in the data from each participant and add to the data frame
motr_df <- data.frame()
for(name in data_names){
  subj <- gsub("reader_", "", gsub("_reading_measures.csv", "", name))
  temp_df <- read.csv(paste0(data_path, "/", name)) %>% mutate(subj_id = subj)
  motr_df <- rbind(motr_df, temp_df)
} 

motr_df <- motr_df %>% mutate(word_len = nchar(word),
                              word_length = scale(word_len)[,1]) %>% 
  group_by(subj_id, item_id) %>%
  arrange(subj_id, item_id) %>%
  mutate(word_len_pre1 = lag(word_length, n = 1),
         word_len_pre2 = lag(word_length, n = 2)) %>%
  ungroup()

# Clean the data
motr <- motr_df %>%
  # filter(subj_id != 171) %>%   # acc = 0.8
  filter(! list %in% c(98, 99)) %>% # filter practice and filler items
  mutate(skip = ifelse(total_duration==0, 1, 0),
         FPReg = ifelse(gaze_duration==0, NA, FPReg),
         FPFix = ifelse(gaze_duration==0, NA, FPFix)) %>%
  filter(skip == 0) %>%
  gather(measure, value, 18:26) %>%
  mutate(tgt_zero = if_else(measure %in% c("first_duration", "gaze_duration", "go_past_time", "right_bounded_rt", "total_duration") & value == 0, F, T)) %>%
  filter(tgt_zero != F) %>%
  dplyr::select(-tgt_zero, -cond_id, -skip, -word_len) %>%
  mutate(item_id = as.factor(item_id),
         subj_id = as.factor(subj_id)) %>%
  spread(measure, value) %>%
  
  # Note: we commented these lines out when running models because we logged the data and used mix effects to account for the variances and noises. If also filter outliers when running models, the results will not change qualitatively, but the estimated CI (or CrI) will be a bit narrower.
  # We filter outliers only for aesthetic reasons in plotting.
  
  gather(measure, value, c("first_duration", "gaze_duration", "go_past_time", "right_bounded_rt", "total_duration")) %>%
  mutate(outlier = value > (mean(value, na.rm = TRUE) + 3 * sd(value, na.rm = TRUE))) %>%
  filter(outlier == FALSE) %>%
  dplyr::select(-outlier) %>%
  spread(measure, value) %>%
  
  gather(measure, value, 21:29) %>%
  mutate(cond = case_when(
    target_gender == "M" & gender_match == "Mis" & type == "stim_adj" ~ "a",
    target_gender == "M" & gender_match == "Mis" & type == "stim_verb" ~ "b",
     target_gender == "M" & gender_match == "Mis" & type == "stim_pred_adj" ~ "c",
    target_gender == "M" & gender_match == "Match" & type == "stim_adj" ~ "d",
    target_gender == "M" & gender_match == "Match" & type == "stim_verb" ~ "e",
    target_gender == "M" & gender_match == "Match" & type == "stim_pred_adj" ~ "f",
    target_gender == "F" & gender_match == "Mis" & type == "stim_adj" ~ "g",
    target_gender == "F" & gender_match == "Mis" & type == "stim_verb" ~ "h",
    target_gender == "F" & gender_match == "Mis" & type == "stim_pred_adj" ~ "i",
    target_gender == "F" & gender_match == "Match" & type == "stim_adj" ~ "j",
    target_gender == "F" & gender_match == "Match" & type == "stim_verb" ~ "k",
    target_gender == "F" & gender_match == "Match" & type == "stim_pred_adj" ~ "l",
    TRUE ~ NA_character_ # This is the default case if none of the above conditions are met
  )) %>%
  dplyr::select(-list, -part, -type_id, -orig_item_number, -case, -animacy, -response_true, -response_chosen) %>%
  mutate(word = str_replace_all(word, "\\.", "")) %>%
  rowwise() %>%
  mutate(log_freq = ifelse(word %in% et_raw$IA_LABEL, 
                           et_raw$lg_frequency[match(word, et_raw$IA_LABEL)], 
                           NA_real_)) %>%
  ungroup()

# View(motr)

```

# ACC by participant
```{r CORRECTNESS SUBJ, eval=FALSE}
motr_acc <- motr %>% dplyr::select(item_id, cond, subj_id, correctness) %>%
  filter(correctness != 99) %>%
  distinct()

motr_acc_summary <- motr_acc %>%
  group_by(subj_id) %>%
  summarise(mean_correctness = mean(correctness),
            sd_correctness = sd(correctness),
            count = n())

# only subj_id 171 get acc = 0.8; others all > 0.88 (incl. fillers)

# write.csv(motr_acc_summary, "./stats/correctness_summary.csv", row.names = FALSE)

et_acc <- et %>% dplyr::select(all_of(c("item_id", "subj_id", "cond", "ACCURACY"))) %>%
  # filter(ACCURACY != -1) %>%
  distinct() %>%
  mutate(correctness = as.numeric(unlist(ACCURACY)))

et_acc_summary <- et_acc %>%
  group_by(subj_id) %>%
  summarise(mean_correctness = mean(correctness),
            sd_correctness = sd(correctness),
            count = n())

# only subj_id russ34 get acc = 0.6; others all > 0.8 (excl. fillers)

```

# ACC by item
```{r CORRECTNESS COND, eval=FALSE}
motr_acc_cond <- motr_acc %>%
  group_by(cond) %>%
  summarise(
    mean_correctness = round(mean(correctness), 2),
    sd_correctness = round(sd(correctness), 2),
    count = n()
  )
motr_acc_cond

et_acc_cond <- et_acc %>%
  group_by(cond) %>%
  summarise(
    mean_correctness = round(mean(correctness), 2),
    sd_correctness = round(sd(correctness), 2),
    count = n()
  )

et_acc_cond
```

### RESEARCH QUESTIONS:
 1. Are RTs significantly different between gender-match and gender-mismatch conditions?
 ==> main effect of grammaticality (gender match or not)
 
 2. Are RTs different in Masculine versus Feminine sentence conditions?
 ==> main effect of gender of target word
 
 3. Are RTs affected by lexical category (whether different lexical categories of the agreeing element will make the processing more difficult or not)? --> ADJ(adj & pre_adj) v.s. VERB 
 ==> main effect of lexical type of sentences. 
 
 4. Are RTs affected by feature matching mechanism(whether agreeing element instantiates internal v.s. external agreement will make a difference in processing difficulty)? --> External (verb & predicative adjective) v.s. Internal (modifying adjective) 
 ==> main effect of syntax type of sentences.
 
 5. Whether the effect of grammaticality is modulated by lexical category? --> Is the RT difference caused by grammaticality effect affected by the target word being Adj or Verb? ==> interaction between grammaticality and lexical type
 
 6. Whether the effect of grammaticality depends on the feature matching mechanism of the sentence? --> Is the RT difference caused by grammaticality effect affected by the mechanism being external or internal? ==> interaction between grammaticality and feature matching mechanism
 
 7. Does the (possible) difference in the sensitivity to the grammaticality manipulation of Masculine versus Feminine conditions differ between lexical category (Adj v.s. Verb)? ==> 3-way interaction between grammaticality, gender and lexical category
 
 8. Does the (possible) difference in the sensitivity to the grammaticality manipulation of Masculine versus Feminine conditions differ between feature matching mechanism (External v.s. Internal)? ==> 3-way interaction between grammaticality, gender and feature matching mechanism

# contrast coding
```{r factorize, echo=TRUE, eval=TRUE}
# check conditions
et$cond <- factor(et$cond)
levels(et$cond)
motr$cond <- factor(motr$cond)
levels(motr$cond)
```

# Create hypothesis matrix from RQ
```{r}
## sol1 
X_H <- matrix(c(1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12, # Intercept
                1/6,1/6,1/6,-1/6,-1/6,-1/6,1/6,1/6,1/6,-1/6,-1/6,-1/6, # Main effect of grammaticality
                1/6,1/6,1/6,1/6,1/6,1/6,-1/6,-1/6,-1/6,-1/6,-1/6,-1/6, # Main effect of gender
                1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8, # Main effect of feature matching
                -1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8, # Main effect of lexical category
                1/6,1/6,1/6,-1/6,-1/6,-1/6,-1/6,-1/6,-1/6,1/6,1/6,1/6, # gram x gen
                1/4,-1/8,-1/8,-1/4,1/8,1/8,1/4,-1/8,-1/8,-1/4,1/8,1/8, # gram x synt
                -1/8,1/4,-1/8,1/8,-1/4,1/8,-1/8,1/4,-1/8,1/8,-1/4,1/8, # gram x lex
                1/4,-1/8,-1/8,1/4,-1/8,-1/8,-1/4,1/8,1/8,-1/4,1/8,1/8,  #gen x synt
                -1/8,1/4,-1/8,-1/8,1/4,-1/8,1/8,-1/4,1/8,1/8,-1/4,1/8, # gen x lex
                1/2,-1/4,-1/4,-1/2,1/4,1/4,-1/2,1/4,1/4,1/2,-1/4,-1/4,  # gram x gen x synt
                -1/4,1/2,-1/4,1/4,-1/2,1/4,1/4,-1/2,1/4,-1/4,1/2,-1/4 # gram x gen x lex
                
), byrow=TRUE, nrow = 12)
# X_H
# rowSums(X_H) # ensure centering

X_C = ginv(X_H)
rownames(X_C) <- c('a','b','c','d','e','f','g','h', 'i', 'j', 'k', 'l')
colnames(X_C) <- c('Int','Gram','Gen','Lex','Synt','Gram_x_Gen','Gram_x_Lex','Gram_x_Synt','Gen_x_Lex','Gen_x_synt','Gram_x_Gen_Lex','Gram_x_Gen_Synt')
X_C_bar <- X_C[,2:ncol(X_C)]
fractions(X_C_bar)
```

```{r Custom contrasts, echo=TRUE, eval=TRUE}
contr_motr <- motr %>% 
  mutate(
    #--------------------- main effects ---------------------
    Gram = ifelse(cond %in% c('a', 'b', 'c', 'g', 'h', 'i'), 1/2, -1/2), # Main effect grammaticality 
    Gen = ifelse(cond %in% c('a','b','c','d','e', 'f'), 1/2, -1/2), # Main effect gender
    Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of feature matching  (a vs pv)
    Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of lexical category (ap vs v)
    
    #--------------------- 2-way interactions ---------------------
    Gram_x_Gen = ifelse(cond %in% c('a', 'b', 'c', 'j', 'k', 'l'), 1/2, -1/2), # Grammaticality x Gender
    Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'g', 'j'), -2/3, 2/3)), # Gender x Feature matching
    Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'h', 'k'), -2/3, 2/3)), # Gender x Lexical category
    Gram_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'i', 'j'), -2/3, 2/3)), # Grammaticality x Feature matching
    Gram_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'i', 'k'), -2/3, 2/3)), # Grammaticality x Lexical Category
    
    #--------------------- 3 way interection ---------------------
    Gram_x_Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'g', 'l'), -1/3, 1/3)), # gen x synt(ap v) x gram
    Gram_x_Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'h', 'l'), -1/3, 1/3)) # gen x lex(ap v) x gram
  ) %>% spread(measure, value) #%>%
  # # filter(word_nr == 3)
  # filter(AOI_id == "R3")
  
contr_motr

contr_et <- et %>% 
  mutate(
    #--------------------- main effects ---------------------
    Gram = ifelse(cond %in% c('a', 'b', 'c', 'g', 'h', 'i'), 1/2, -1/2), # Main effect grammaticality 
    Gen = ifelse(cond %in% c('a','b','c','d','e', 'f'), 1/2, -1/2), # Main effect gender
    Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of feature matching  (a vs pv)
    Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of lexical category (v vs ap)
    
    #--------------------- 2-way interactions ---------------------
    Gram_x_Gen = ifelse(cond %in% c('a', 'b', 'c', 'j', 'k', 'l'), 1/2, -1/2), # Grammaticality x Gender
    Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'g', 'j'), -2/3, 2/3)), # Gender x Feature matching
    Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'h', 'k'), -2/3, 2/3)), # Gender x Lexical category
    Gram_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'i', 'j'), -2/3, 2/3)), # Grammaticality x Feature matching
    Gram_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'i', 'k'), -2/3, 2/3)), # Grammaticality x Lexical Category
    
    #--------------------- 3 way interection ---------------------
    Gram_x_Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'g', 'l'), -1/3, 1/3)), # gen x synt(ap v) x gram
    Gram_x_Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'h', 'l'), -1/3, 1/3)) # gen x lex(ap v) x gram
  ) %>% spread(measure, value) %>%
  rename(RegIn_incl = RegIn)
# View(contr_et)
```

```{r Store data for Power Analysis, echo=TRUE, eval=False}
# write.csv(contr_et, "./stats/et_reading_measures_contrast_coded.csv", row.names = FALSE)
# write.csv(contr_motr, "./stats/motr_reading_measures_contrast_coded.csv", row.names = FALSE)
```


```{r Constrasts sanity check, echo=TRUE, eval=False}
## sol2 --> try hypr package, also for sanity check
hypothesis_matrix <- hypr(
  Gram = (a+b+c+g+h+i)/6 ~ (d+e+f+j+k+l)/6,
  Gen = (a+b+c+d+e+f)/6 ~ (g+h+i+j+k+l)/6,
  Synt = (a+d+g+j)/4 ~ (b+c+e+f+h+i+k+l)/8,
  Lex = (b+e+h+k)/4 ~ (a+c+d+f+g+i+j+l)/8,
  # Gram_x_Gen = ((a+b+c)/3-(d+e+f)/3)/2 ~ ((g+h+i)/3-(j+k+l)/3)/2,
  Gram_x_Synt = ((e+f+k+l)/4-(d+j)/2)/2 ~ ((b+c+h+i)/4-(a+g)/2)/2 ,
  Gram_x_Lex =  ((d+f+j+l)/4-(e+k)/2)/2 ~ ((a+c+g+i)/4-(b+h)/2)/2,
  # Gen_x_Synt = ((h+i+k+l)/4-(g+j)/2)/2 ~ ((b+c+e+f)/4-(a+d)/2)/2,
  # Gen_x_Lex = ((g+i+j+l)/4-(h+k)/2)/2 ~ ((a+c+d+f)/4-(b+e)/2)/2,
  Gram_x_Gen_x_Synt = (((h+i)/2-g)-((k+l)/2-j))/2 ~ (((b+c)/2-a)-((e+f)/2-d))/2,
  Gram_x_Gen_x_Lex = (((g+i)/2-h)-((j+l)/2-k))/2 ~ (((a+c)/2-b)-((d+f)/2-e))/2
)

# Display the matrix
hypothesis_matrix
```


```{r freq-modeling, echo=TRUE, eval=FALSE, message=TRUE}

stats_freq = data.frame()
# regions = c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions = c("R3")
measure_types = c("gaze_duration", "go_past_time", "total_duration",
"FPReg", "RegIn_incl"
)

for (meth in methods) {
  for (region in regions) {
    for (meas in measure_types){
      print(paste("Fitting model for:", meas, "in Region:", region))
      if (meas %in% c("first_duration", "gaze_duration", "go_past_time", "total_duration")){
        if (meth == "motr") {
          model <- contr_motr %>% 
            filter(AOI_id == region) %>% 
            filter(!is.na(.data[[meas]]))  %>% 
            lmer(as.formula(paste("log(", meas, ") ~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
                (1 | item_id) + (1 + Gram | subj_id)")),
                data = ., REML = F)
        } else {
          model <- contr_et %>% 
            filter(AOI_id == region) %>% 
            filter(!is.na(.data[[meas]]))  %>% 
            lmer(as.formula(paste("log(", meas, ") ~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
                (1 | item_id) + (1 + Gram | subj_id)")),
                data = ., REML = F)
        }
      coefs <- summary(model)$coefficients
      temp_results <- data.frame(
        method = meth,
        region = region,
        measure = meas,
        beta = c("b_0", "b_Gram", "b_Gen", "b_Lex", "b_Synt", 
                 "b_Gram_x_Lex", "b_Gram_x_Synt", "b_Gram_x_Gen_x_Lex", "b_Gram_x_Gen_x_Synt"),
        bval = coefs[, "Estimate"],
        pval = coefs[, "Pr(>|t|)"]
          )
      }else{
        if (meth == "motr") {
          model <- contr_motr %>% filter(!is.na(.data[[meas]]))  %>% 
            glmer(as.formula(paste(meas, "~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt + 
                (1 | item_id) + (1 | subj_id)")), 
                data = ., family=binomial(link = "logit"))
        } else{
          model <- contr_et %>% filter(!is.na(.data[[meas]]))  %>% 
            glmer(as.formula(paste(meas, "~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt + 
                (1 | item_id) + (1 | subj_id)")), 
                data = ., family=binomial(link = "logit"))
        }
      coefs <- summary(model)$coefficients
      temp_results <- data.frame(
        method = meth,
        region = region,
        measure = meas,
        beta = c("b_0", "b_Gram", "b_Gen", "b_Lex", "b_Synt", 
                 "b_Gram_x_Lex", "b_Gram_x_Synt", "b_Gram_x_Gen_x_Lex", "b_Gram_x_Gen_x_Synt"),
        bval = coefs[, "Estimate"],
        pval = coefs[, "Pr(>|z|)"]
        )
      }
    stats_freq = rbind(stats_freq, temp_results)
  }
}
}
stats_freq <- stats_freq %>%
  mutate(sig = case_when(
    pval < 0.001 ~ "***",
    pval < 0.01  ~ "**",
    pval < 0.05  ~ "*",
    pval < 0.1   ~ ".",
    TRUE         ~ ""
  ))

# View(stats_freq)

# write.csv(stats_freq, "./stats/stats_motr_et_lmer.csv", row.names = FALSE)

```


# Fit Bayesian models
# function for creating stan data format
```{r createStanData, echo=TRUE, eval=FALSE}
createStanData <-function(d, dv,form){
  
  subj <- as.integer(factor(d$subj_id))
  N_subj <- length(unique(subj))
  item <- as.integer(factor(d$item_id))
  N_items <- length(unique(item))
  X <- unname(model.matrix(form, d))  
  attr(X, which="assign") <- NULL
  
  stanData <- list(N = nrow(X),           
                  P = ncol(X),              
                  n_u = ncol(X),             
                  n_w = 3,            
                  X = X,                     
                  Z_u = X,                 
                  Z_w = X[, 1:3, drop = FALSE],    # only by-item random intercept, Gram, Gen              
                  J = N_subj,                
                  K = N_items,
                  dv = dv,                    
                  subj = subj,
                  item = item)
  stanData
}
```

```{r bayesian-modeling, echo=TRUE, eval=FALSE, message=TRUE}
# note: this chunk takes time to run ~ 1 hour for one region

# regions <- c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions <- c("R3")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
# measure_types <- c("go_past_time")

for (meth in methods) {
  for (region in regions) {
    for (meas in measure_types) {
      print(paste("Fitting Bayesian model for:", meas, "in Region:", region))
      if (meth == "motr"){
        # Filter data for current region and non-missing measure
        temp <- contr_motr %>%
          filter(AOI_id == region) %>%
          filter(!is.na(.data[[meas]]))
      } else {
        temp <- contr_et %>%
          filter(AOI_id == region) %>%
          filter(!is.na(.data[[meas]]))
      }
      # binary dv
      if (meas %in% c("FPReg", "RegIn_incl")) {
        stan_data <- createStanData(
          d = temp,
          form = as.formula("~1 + Gram + Gen + Synt + Lex + Gram_x_Synt + Gram_x_Lex + Gram_x_Gen_x_Synt + Gram_x_Gen_x_Lex"), 
          dv = temp[[meas]]
        )
       stan_model_file <- "stan/Model_binary.stan"
      } else {
        # For other measures, use the default formulas and models
        stan_data <- createStanData(
          d = temp,
          form = as.formula("~1 + Gram + Gen + Synt + Lex + Gram_x_Synt + Gram_x_Lex + Gram_x_Gen_x_Synt + Gram_x_Gen_x_Lex"), 
          dv = temp[[meas]]
        )
        stan_model_file <- "stan/Model_RT.stan"
      }
      # Fit model 
      stan_model <- stan(
        file = stan_model_file, 
        data = stan_data,
        iter = 4000, 
        chains = 4,
        control = list(adapt_delta = 0.99)
      )
      # Save model output
      model_save_path <- paste0("models/", meth, "_", meas, "_", region, ".rds")
      saveRDS(stan_model, file = model_save_path)
    }
  }
}

```


# Examine fitted stan models
```{r Model_examination, echo=TRUE, eval=False}
# change xx.rds to other models to check them
region <- "R3"
meas <- "go_past_time"

model_path <- paste0("models/et_", meas, "_", region, ".rds")
m1_gd <- readRDS(model_path)

summary(m1_gd)
# check params
summary(m1_gd, pars = c('beta[1]', 'beta[2]', 'beta[3]', 'beta[4]', 'beta[5]', 'beta[6]', 'beta[7]', 'beta[8]', 'beta[9]'))

# check convergence
traceplot(m1_gd, pars = c("beta"))

# check predicts --> posterior parameter distr.
y_posterior <- extract(m1_gd) 
y_posterior$beta[,1] #intercept
y_posterior$beta[,2] #Gram
y_posterior$beta[,3] #Gen
y_posterior$beta[,4] #Synt
y_posterior$beta[,5] #Lex
y_posterior$beta[,6] #Gram_x_Synt
y_posterior$beta[,7] #Gram_x_Lex

# check predicts --> posterior parameter distr. back in normal space
pst_gram <- y_posterior$Gram
pst_gram
density_gram <- density(pst_gram)
plot(density_gram, main = "Density Plot of pst_gram", xlab = "pst_gram values", ylab = "Density", col = "red")

pst_gen <- y_posterior$Gen
pst_gen
density_gen <- density(pst_gen)
plot(density_gen, main = "Density Plot of pst_gen", xlab = "pst_gen values", ylab = "Density", col = "red")

pst_lex <- y_posterior$Lex
pst_lex
density_lex <- density(pst_lex)
plot(density_lex, main = "Density Plot of pst_lex", xlab = "pst_lex values", ylab = "Density", col = "red")

pst_synt <- y_posterior$Synt
pst_synt
density_synt <- density(pst_synt)
plot(density_synt, main = "Density Plot of pst_synt", xlab = "pst_synt values", ylab = "Density", col = "red")

pst_gramxlex <- y_posterior$Gram_x_Lex
pst_gramxlex
density_gramxlex <- density(pst_gramxlex)
plot(density_gramxlex, main = "Density Plot of pst_gramxlex", xlab = "pst_gramxlex values", ylab = "Density", col = "red")

pst_gramxsynt <- y_posterior$Gram_x_Synt
pst_gramxsynt
density_gramxsynt <- density(pst_gramxsynt)
plot(density_gramxsynt, main = "Density Plot of pst_gramxsynt", xlab = "pst_gramxtyps values", ylab = "Density", col = "red")

pst_gramxgenxlex <- y_posterior$Gram_x_Gen_x_Lex
pst_gramxgenxlex
density_gramxgenxlex <- density(pst_gramxgenxlex)
plot(density_gramxgenxlex, main = "Density Plot of pst_gramxgenxlex", xlab = "pst_gramxgenxlex values", ylab = "Density", col = "red")


pst_gramxgenxsynt <- y_posterior$Gram_x_Gen_x_Synt
pst_gramxgenxsynt
density_gramxgenxsynt <- density(pst_gramxgenxsynt)
plot(density_gramxgenxsynt, main = "Density Plot of pst_gramxgenxsynt", xlab = "pst_gramxgenxsynt values", ylab = "Density", col = "red")


# check posterior predicts --> the fits looks very good --> suspision of overfit? --> cv validation?
predicts <- y_posterior$Predict_rt
dim(predicts) # 8000 x 1271
y_true <- contr_et %>%
  filter(AOI_id == region) %>%             # Filter by the specified region
  filter(!is.na(.data[[meas]])) %>%        # Filter out NA values for the specific measure
  pull(.data[[meas]]) 

ppc_dens_overlay(y_true, yrep = predicts[1:200, ])

```

# compile model results
```{r compile_restuls, echo=TRUE, eval=TRUE}
stats_df <- data.frame()

# regions <- c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions <- c("R3")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")

for (meth in methods) {
  # Loop over each measure type to read the corresponding model and extract data
  for (region in regions) {
    for (meas in measure_types) {
      model_path <- paste0("models/", meth, "_", meas, "_", region, ".rds")
      m1 <- readRDS(model_path)
      # print(summary(m1))
      # Extract posterior distributions
      y_posterior <- extract(m1)
      intercept <- exp(y_posterior$beta[,1])
    
      betas <- c("b_0", "b_Gram", "b_Gen","b_Synt", "b_Lex",
                 "b_Gram_x_Synt", "b_Gram_x_Lex", "b_Gram_x_Gen_x_Synt", "b_Gram_x_Gen_x_Lex")
      posterior_samples <- list(intercept, y_posterior$Gram, y_posterior$Gen, y_posterior$Synt, y_posterior$Lex,
                                y_posterior$Gram_x_Synt, y_posterior$Gram_x_Lex, y_posterior$Gram_x_Gen_x_Synt, y_posterior$Gram_x_Gen_x_Lex)
      
      hpdi_95 <- lapply(posterior_samples, function(x) hdi(x, credMass = 0.95))
      hpdi_89 <- lapply(posterior_samples, function(x) hdi(x, credMass = 0.89))
    
      # Prepare the results data frame
      temp_results <- data.frame(
        method = rep(meth, length(betas)),
        region = rep(region, length(betas)),
        measure = rep(meas, length(betas)),
        beta = betas,
        bval_mean = sapply(posterior_samples, mean),
        crI_95_lower = sapply(posterior_samples, function(x) quantile(x, 0.025)),
        crI_95_upper = sapply(posterior_samples, function(x) quantile(x, 0.975)),
        crl_89_lower = sapply(posterior_samples, function(x) quantile(x, 0.055)),
        crl_89_upper = sapply(posterior_samples, function(x) quantile(x, 0.945)),
        hpdi_95_lower = sapply(hpdi_95, function(x) x[1]),
        hpdi_95_upper = sapply(hpdi_95, function(x) x[2]),
        hpdi_89_lower = sapply(hpdi_89, function(x) x[1]),
        hpdi_89_upper = sapply(hpdi_89, function(x) x[2]),
        bval_median = sapply(posterior_samples, median)
      )
    
      # Append the temp_results to the stats_df data frame
      stats_df <- rbind(stats_df, temp_results)
    }
  }
  
}

# View(stats_df)

stats_df <- stats_df %>%
  mutate(across(
    where(is.numeric),
    ~ if_else(measure %in% c("FPReg", "RegIn_incl"), round(., 3), round(., 0))
  )) %>% 
  mutate(
    annotation = paste0(round(bval_mean, 2), 
                    " [", round(crI_95_lower, 2), ", ", 
                    round(crI_95_upper, 2), "]")
  )
write.csv(stats_df, "./stats/stats_bayesian.csv", row.names = FALSE)
```


```{r Model Sanity check, eval=FALSE}
regions <- c("R2", "R3", "R4", "R5")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
groups <- c("gender_match", "target_gender", "lex", "synt")

all_diffs <- data.frame()

# Loop through each region, measure type, and group
for (region in regions) {
  for (meas in measure_types) {
    for (group in groups) {
      
      # Calculate mean and difference for each subgroup
      summary_stats <- contr_motr %>%
        mutate(lex = if_else(type=="stim_verb", "v", "a"),
               synt = if_else(type=="stim_adj", "in", "ex")) %>%
        filter(AOI_id == region) %>%            # Filter by region
        filter(!is.na(.data[[meas]])) %>%       # Filter out NA values for measure
        group_by(.data[[group]]) %>%            # Group by current group variable
        summarise(mean_value = mean(.data[[meas]], na.rm = TRUE)) %>%  # Mean of measure
        summarise(diff = diff(mean_value))      # Difference between the means
      
      # Append the results to all_diffs
      all_diffs <- rbind(all_diffs, 
                         data.frame(
                           region = region, 
                           measure_type = meas, 
                           group = group, 
                           diff = summary_stats$diff
                         ))
    }
  }
}

all_diffs
```

# PLOT

```{r PREP PLOT, eval=TRUE}
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")

# prepare motr for plotting
motr_plot <- contr_motr %>%
  dplyr::select(item_id, type, target_gender, gender_match, word_nr, word, AOI_id, subj_id, cond, gaze_duration, go_past_time, total_duration, FPReg, RegIn_incl) %>%
  mutate(region = as.double(substr(AOI_id, 2, 2))) %>%
  mutate(synt = ifelse(type %in% c('stim_adj'), "Internal", "External"),
         lex = ifelse(type %in% c('stim_verb'), "Verb", "Adjective")
         ) %>%
  drop_na(total_duration) %>%
  gather(measure, value, measure_types) %>%
  filter(region %in%c(2, 3, 4, 5)) %>%
  drop_na()

# View(motr_plot)

motr_lex <- motr_plot %>%
  group_by(lex, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(lex, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(lex, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>%
  ungroup() %>%
  mutate(lex = factor(lex, levels=c("Adjective", "Verb"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Lexical Category",
          method = "MoTR") %>%
  rename(type = lex) 

motr_synt <- motr_plot %>%
  group_by(synt, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(synt, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(synt, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>% 
  ungroup() %>%
  mutate(synt = factor(synt, levels=c("Internal", "External"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Agreement Type",
         method = "MoTR") %>%
  rename(type = synt)

# plot et data for plotting 
et_plot <- contr_et %>%
  dplyr::select(item_id, type, target_gender, gender_match, word_nr, word, AOI_id, subj_id, cond, gaze_duration, go_past_time, total_duration, FPReg, RegIn_incl) %>%
  mutate(region = as.double(substr(AOI_id, 2, 2))) %>%
  mutate(synt = ifelse(type %in% c('stim_adj'), "Internal", "External"),
         lex = ifelse(type %in% c('stim_verb'), "Verb", "Adjective")
         ) %>%
  drop_na(total_duration) %>%
  gather(measure, value, measure_types) %>%
  filter(region %in%c(2, 3, 4, 5)) %>%
  drop_na()

et_lex <- et_plot %>%
  group_by(lex, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(lex, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(lex, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>%
  ungroup() %>%
  mutate(lex = factor(lex, levels=c("Adjective", "Verb"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Lexical Category",
         method = "Eye-tr.") %>%
  rename(type = lex)

et_synt <- et_plot %>%
  group_by(synt, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(synt, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(synt, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>% 
  ungroup() %>%
  mutate(synt = factor(synt, levels=c("Internal", "External"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Agreement Type",
         method = "Eye-tr.") %>%
  rename(type = synt)

motr_et_plot <- rbind(motr_lex, motr_synt, et_lex, et_synt)

stats_df <- read_csv("./stats/stats_bayesian.csv", show_col_types = FALSE)

annotation <- stats_df %>% 
  mutate(region = as.double(substr(region, 2, 2)),
         measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob.")),
          method = if_else(method=="motr", "MoTR", "Eye-tr."))%>% 
  filter(beta %in% c("b_Gram_x_Lex", "b_Gram_x_Synt")) %>%
  mutate(Prediction = if_else(beta == "b_Gram_x_Lex", "Lexical Category", "Agreement Type")) %>%
  dplyr::select(method, region, measure, Prediction, annotation)
View(annotation)

```

```{r}
plot_annotated <- motr_et_plot %>%
  left_join(annotation, by = c("method", "region", "measure", "Prediction")) %>%
  mutate(annotation = if_else(is.na(annotation), "", annotation)) %>%
  mutate(annotation = if_else(type %in% c("Verb", "External"), "", annotation))

plot_annotated
```


```{r}
plot_annotated %>%
  filter(method == "MoTR") %>%
  filter(measure %in% c("Gaze Duration", "Go Past Time", "Total Duration")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction)) +
    geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = lower - 100, ymax = upper + 100), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point(aes(shape = type)) +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 50), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Reading time difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "External" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "MoTR",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = guide_legend(
      title = "MoTR",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid")
      )
    )
  )
ggsave(paste0("./images/motr_rt_interaction.pdf"), device="pdf", height=6, width=8)
```

```{r}
plot_annotated %>%
  filter(method == "MoTR") %>%
  filter(measure %in% c("First Pass Regression out Prob.", "Regression in Prob.")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction, shape = type)) +
    geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = 0, ymax = upper + 0.2), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point() +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 0.1), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Regression prob. difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "Agreement" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = "none"
  )
ggsave(paste0("./images/motr_regression_interaction.pdf"), device="pdf", height=6, width=16/3)
```

```{r}
plot_annotated %>%
  filter(method == "Eye-tr.") %>%
  filter(measure %in% c("Gaze Duration", "Go Past Time", "Total Duration")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction)) +
  geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = lower - 100, ymax = upper + 100), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point(aes(shape = type)) +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 50), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Reading time difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "External" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid")
      )
    )
  )
ggsave(paste0("./images/et_rt_interaction.pdf"), device="pdf", height=6, width=8)
```

```{r}
plot_annotated %>%
  filter(method == "Eye-tr.") %>%
  filter(measure %in% c("First Pass Regression out Prob.", "Regression in Prob.")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction, shape = type)) +
    geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = 0, ymax = upper + 0.2), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point() +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 0.1), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Regression prob. difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "Agreement" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = "none"
  )
ggsave(paste0("./images/et_regression_interaction.pdf"), device="pdf", height=6, width=16/3)
```




